IU - User contributions [en]

BSc: Introduction To Programming

2022-11-11T07:59:02Z

R.akhmetzyanov: /* The retake exam */

= Introduction to Programming =
* '''Course name''': Introduction to Programming
* '''Code discipline''': CSE101
* '''Subject area''': Programming Languages and Software Engineering

== Short Description ==
This course covers the following concepts: Basic concept - algorithm, program, data; Computer architecture basics; Structured programming; Object-oriented programming; Generic programming; Exception handling; Programming by contract (c); Functional programming; Concurrent programming.

== Prerequisites ==

=== Prerequisite subjects ===

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Introduction to programming ||
# Basic definitions – algorithm, program, computer, von Neumann architecture, CPU lifecycle.
# Programming languages history and overview. Imperative (procedural) and functional approaches.
# Translation – compilation vs. interpretation. JIT, AOT. Hybrid modes.
# Introduction to typification. Static and dynamic typing. Type inference. Basic types – integer, real, character, boolean, bit. Arrays and strings. Records-structures.
# Programming – basic concepts. Statements and expressions. 3 atomic statements - assignment, if-check, goto. Control structures – conditional, assignment, goto, case-switch-inspect, loops.
# Variables and constants.
# Routines – procedures and functions.
|-
| Introduction to object-oriented programming ||
# Key principles of object-oriented programming
# Overloading is not overriding
# Concepts of class and object
# How objects can be created?
# Single and multiple inheritance
|-
| Introduction to generics, exception handling and programming by contract (C) ||
# Introduction to generics
# Introduction to exception handling
# Introduction to programming by contract (C)
|-
| Introduction to programming environments ||
# Concept of libraries as the basis for reuse.
# Concept of interfaces/API. Separate compilation.
# Approaches to software documentation.
# Persistence. Files.
# How to building a program. Recompilation problem. Name clashes, name spaces
|-
| Introduction to concurrent and functional programming ||
# Concurrent programming.
# Functional programming within imperative programming languages.
|}
== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The Introduction to Programming course teaches the fundamental concepts and skills necessary to perform programming at a professional level. Students will learn how to master the fundamental control structures, data structures, reasoning patterns and programming language mechanisms characterizing modern programming, as well as the fundamental rules of producing high-quality software. They will acquire the necessary programming background for later courses introducing programming skills in specialized application areas. The course focuses on Object Oriented paradigm.

=== ILOs defined at three levels ===

==== Level 1: What concepts should a student know/remember/explain? ====
By the end of the course, the students should be able to ...
* Basic concepts of programming. What is algorithm, program.
* Concept of typification. Dynamic and static types.
* Concepts of structured programming, object-oriented one.
* Concepts of exception handling and generic programming.
* Concurrent programming and functional programming in imperative programming languages.
* verification of the software based on programming by contract (C)

==== Level 2: What basic practical skills should a student be able to perform? ====
By the end of the course, the students should be able to ...
* How to create high quality software using mainstream concepts of programming.
* What is object-oriented programming and its main advantages
* How to increase the level of abstraction with help of genericity.
* How to create concurrent programs and what are the main issues related to this kind of programming

==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
By the end of the course, the students should be able to ...
* To be able to create quality programs in Java.
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 75-84 || -
|-
| C. Satisfactory || 60-75 || -
|-
| D. Poor || 0-59 || -
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Labs/seminar classes || 40
|-
| Interim performance assessment || 30
|-
| Exams || 30
|}

=== Recommendations for students on how to succeed in the course ===

== Resources, literature and reference materials ==

=== Open access resources ===

=== Closed access resources ===

=== Software and tools used within the course ===

= Teaching Methodology: Methods, techniques, & activities =

== Activities and Teaching Methods ==
{| class="wikitable"
|+ Activities within each section
|-
! Learning Activities !! Section 1 !! Section 2 !! Section 3 !! Section 4 !! Section 5
|-
| Homework and group projects || 1 || 1 || 1 || 1 || 1
|-
| Midterm evaluation || 1 || 1 || 1 || 0 || 0
|-
| Testing (written or computer based) || 1 || 0 || 0 || 1 || 1
|-
| Oral polls || 1 || 1 || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1 || 1 || 1
|-
| Development of individual parts of software product code || 0 || 1 || 0 || 0 || 0
|-
| Reports || 0 || 1 || 0 || 0 || 0
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What is the difference between compiler and interpreter? || 1
|-
| Question || What is the difference between type and variable? || 1
|-
| Question || What is the background of structured programming? || 1
|-
| Question || How to compile a program? || 0
|-
| Question || How to run a program? || 0
|-
| Question || How to debug a program? || 0
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What is the meaning of polymorphism? || 1
|-
| Question || How to check the dynamic type of an object? || 1
|-
| Question || What are the limitations of single inheritance? || 1
|-
| Question || What are the issues related with multiple inheritance? || 1
|-
| Question || How to handle array of objects of some class type? || 0
|-
| Question || How to implement the class which logically has to have 2 constructors with the same signature but with different semantics? || 0
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What is constrained genericity? || 1
|-
| Question || What is exception? || 1
|-
| Question || What is assertion? || 1
|-
| Question || How constrained genericity may be used for sorting of objects? || 0
|-
| Question || In which order catch blocks are being processed? || 0
|-
| Question || Where is the problem when precondition is violated? || 0
|}
==== Section 4 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How reuse helps to develop software? || 1
|-
| Question || How concept of libraries and separate compilation co-relate? || 1
|-
| Question || What are the benefits of integrating documentation into the source code? || 1
|-
| Question || Why is it essential to have persistent data structures? || 1
|-
| Question || What is to be done to design and develop a library? || 0
|-
| Question || How to add documenting comments into the source code? || 0
|-
| Question || What ways exists in Java to support persistence ? || 0
|}
==== Section 5 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || Explain the key differences parallelism and concurrency || 1
|-
| Question || What are the key issues related to parallel execution? || 1
|-
| Question || What are the models of parallel execution? || 1
|-
| Question || What is the difference between function and object? || 1
|-
| Question || Which Java construction support concurrency? || 0
|-
| Question || What is a thread? || 0
|-
| Question || What is in-line lambda function? || 0
|}
=== Final assessment ===
'''Section 1'''
# What are the basic control structure of structured programming?
# What is the difference between statements and expressions?
# What are the benefits of type inference?
'''Section 2'''
# Name all principles of object-oriented programming?
# Explain what conformance means?
# Explain why cycles are prohibited in the inheritance graph?
'''Section 3'''
# Can array be treated as generic class?
# What is the difference between throw and throws in Java?
# What is purpose of the class invariant?
'''Section 4'''
# How to deal with name clashes?
# What is the main task of the recompilation module?
# What are the differences between different formats of persistence files?
'''Section 5'''
# What is the meaning of SIMD and MIMD?
# What are the implications of the Amdahl’s law?
# What model of concurrency Java relies on?
# Which function can be considered as pure?
# How to declare a function to accept a functional object as its argument?
# How Java supports high-order functions?
# How capturing variables works in Java?

=== The retake exam ===
'''Section 1'''

'''Section 2'''

'''Section 3'''

'''Section 4'''

'''Section 5'''

BSc:Syllabi Index

2022-11-08T11:26:22Z

R.akhmetzyanov: /* SE */

== Technical ==

=== Computer Science, Programming ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming CSE101 — Introduction to Programming I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming_II CSE102 — Introduction to Programming II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Computer_Science CSE103 — Theoretical Computer Science]
* [https://eduwiki.innopolis.university/index.php/BSc:_Operating_Systems CSE105 — Operating Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Databases CSE106 — Databases]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Compilers_Construction_and_Program_Analysis CSE108 — Advanced Compilers Construction and Program Analysis]
* [https://eduwiki.innopolis.university/index.php/BSc:_Programming_Paradigms CSE109 — Programming Paradigms]
* [https://eduwiki.innopolis.university/index.php/BSc:_Software_Systems_Design CSE112 — Software Systems Analysis and Design]
* [https://eduwiki.innopolis.university/index.php/BSc:_Distributed_And_Network_Programming CSE114 — Distributed And Network Programming]
* [https://eduwiki.innopolis.university/index.php/BSc:_Compilers_Construction CSE115 — Compilers Construction]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Structures_Algorithms CSE117 — Data Structures and Algorithms]

=== Maths ===
* [https://eduwiki.innopolis.university/index.php/BSc:Logic_and_Discrete_Mathematics.f22 CSE113 — Philosophy I - (Discrete Math and Logic)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I.f22 CSE201 — Mathematical Analysis I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I.f22 CSE202 — Analytical Geometry and Linear Algebra I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II.s23 CSE203 — Mathematical Analysis II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II.s23 CSE204 — Analytic Geometry And Linear Algebra II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Differential_Equations.f22 CSE205 — Differential Equations]
* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics.f22 CSE206 — Probability And Statistics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_to_Optimization.F22 CSE333 — Introduction to Optimization]

=== AI ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Artificial_Intelligence CSE301 — Philosophy II - Introduction to Artificial Intelligence]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Machine_Learning CSE302 — Introduction to Machine Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Big_Data CSE303 — Introduction to Big Data]
* [https://eduwiki.innopolis.university/index.php/BSc:_Game_Theory CSE304 — Game Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Mining CSE305 — Data Mining]
* [https://eduwiki.innopolis.university/index.php/BSc:_Information_Retrieval CSE306 — Information Retrieval]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Computer_Vision CSE307 — Introduction to Computer Vision]
* [https://eduwiki.innopolis.university/index.php/BSc:_Practical_Machine_Learning_Deep_Learning CSE308 — Practical Machine Learning And Deep Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Statistical_Techniques_For_Data_Science CSE310 — Statistical Techniques for Data Science and Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Natural_Language_Processing CSE311 — Natural Language Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Nature_Inspired_Computing CSE340 — Nature Inspired Computing]
----

=== Hardware and Robotics ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Computer_Architecture CSE401 — Fundamentals of Computer Architecture]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_I CSE402 — Physics I (Mechanics)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Control_Theory CSE403 — Control Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Sensors_And_Sensing CSE404 — Sensors and Sensing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechatronics CSE405 — Mechatronics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Robotics CSE406 — Fundamentals of Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechanics_And_Machines CSE407 — Mechanics And Machines]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Mechanics CSE408 — Theoretical Mechanics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Robotic_Systems CSE409 — Robotic Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_II CSE410 — Physics II - Electrical Engineering]

----

=== Security and Networks ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Networks CSE501 — Networks]
* [https://eduwiki.innopolis.university/index.php/BSc:_System_And_Network_Administration CSE502 — System And Network Administration]
* [https://eduwiki.innopolis.university/index.php/BSc:_Network_And_Cyber_Security CSE503 — Network And Cyber Security]
* [https://eduwiki.innopolis.university/index.php/BSc:_Signals_And_Systems CSE504 — Signals And Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Dev_Ops_Engineering CSE508 — DevOps Engineering]
* [https://eduwiki.innopolis.university/index.php/BSc:_Fundamentals_of_Computer_Security CSE509 — Fundamentals of Information Security]

=== SE ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Lean_Software_Development CSE803 — Lean Software Development]
* [https://eduwiki.innopolis.university/index.php/BSc:SoftwareQualityandReliability.S22 CSE806 — Software Quality and Reliability]
* [https://eduwiki.innopolis.university/index.php/BSc:_It_Product_Development CSE807 — IT Product Development]

== Humanities ==

=== Pedagogy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_History HSS601 — History (History of Russia, World History)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physical_Culture_and_Sport HSS105 — Physical Culture and Sport]

=== Languages and literature ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture HSS203 — Foreign Language I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS204 — Foreign Language II]

=== The science ===
* [https://eduwiki.innopolis.university/index.php/BSc:AcademicResearchandWritingCulture HSS501 — Academic Research and Writing Culture 1]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS502 — Academic Research and Writing Culture 2]

=== History and philosophy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Philosophy_II HSS602 — Philosophy II (Languages and Perceptions)]

----

BSc:Syllabi Table 3+1

2022-09-19T10:58:44Z

R.akhmetzyanov: /* Overview of the BS Program */

= Overview of the BS Program =

[https://eduwiki.innopolis.university/index.php/BSc:Syllabi_Index The detailed description of the program can be found here.]

{| class="wikitable"
|- style="font-weight:bold; text-align:center;"
! colspan="7" style="vertical-align:middle; background-color:#EAECF0; color:#202122;" | Y1-T1
|-
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | Subject
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | SD
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | CS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | DS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | AAI
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | R
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:IntroductionToProgramming CSE101 - Introduction To Programming] [https://moodle.innopolis.university/course/view.php?id=800 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:Logic_and_Discrete_Mathematics.f22 CSE113 - Logic and Discrete Mathematics (Philosophy I)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I.f22 CSE201 - Mathematical Analysis I] [https://moodle.innopolis.university/course/view.php?id=801 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:EnglishForAcademicPurposesI HSS203 - English For Academic Purposes I] [https://moodle.innopolis.university/course/view.php?id=866 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I.f22 CSE204 - Analytic Geometry And Linear Algebra I] [https://moodle.innopolis.university/course/view.php?id=787 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:_Computer_Architecture CSE401 - Computer Architecture] [https://moodle.innopolis.university/course/view.php?id=786 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
! colspan="7" style="vertical-align:middle; background-color:#EAECF0; color:#202122;" | Y1-T2
|-
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | Subject
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | SD
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | CS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | DS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | AAI
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | R
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Computer_Science CSE103 - Theoretical Computer Science] [https://moodle.innopolis.university/course/view.php?id=670 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:_Software_Systems_Design CSE112 - Software System Analysis and Design] [https://moodle.innopolis.university/course/view.php?id=664 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:_Data_Structures_Algorithms CSE117 - Data Structures Algorithms] [https://moodle.innopolis.university/course/view.php?id=671 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II.s23 CSE203 - Mathematical Analysis II] [https://moodle.innopolis.university/course/view.php?id=666 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II.s23 CSE204 - Analytic Geometry And Linear Algebra II] [https://moodle.innopolis.university/course/view.php?id=668 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:EnglishForAcademicPurposesII HSS203 - English For Academic Purposes II] [https://moodle.innopolis.university/course/view.php?id=710 (Moodle)]
| colspan="5" style="vertimiddle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="font-weight:bold; text-align:center;"
| colspan="7" style="vertical-align:middle; background-color:#EAECF0; color:#202122;" | Y1-T3
|-
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | Subject
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | SD
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | CS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | DS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | AAI
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | R
|- style="text-align:center;"
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | Software Project [https://moodle.innopolis.university/course/view.php?id=340 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/List_of_BSc_Technical_Electives Tech Elective 1 - List of BSc Technical Electives]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/List_of_BSc_Humanitarian_Electives Hum Elective 1 - List of BSc Humanitarian Electives]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:PhysicalCultureandSport HSS105 — Physical Culture and Sport] [https://moodle.innopolis.university/course/view.php?id=970 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:History HSS601 - History (History of Russia, World History)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
|- style="font-weight:bold; text-align:center;"
| colspan="7" style="vertical-align:middle; background-color:#EAECF0; color:#202122;" | Y2-T1
|-
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | Subject
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | SD
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | CS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | DS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | AAI
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | R
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:OperatingSystems CSE105 - Operating Systems]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:_Differential_Equations.f22 CSE205 - Differential Equations]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics.f22 CSE206 - Probability And Statistics]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:IntroductionToArtificialIntelligence CSE301 - Introduction To Artificial Intelligence]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:PhysicsI CSE402 - Physics I]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_to_Optimization.F22 Introduction to Optimization]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| colspan="7" style="vertical-align:middle; background-color:#EAECF0; color:#202122;" | Y2-T2
|-
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | Subject
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | SD
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | CS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | DS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | AAI
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | R
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:DataModelingDatabasesII_old CSE106 - Databases] [https://moodle.innopolis.university/course/view.php?id=676 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:DistributedAndNetworkProgramming CSE114 - Distributed and Network Programming] [https://moodle.innopolis.university/course/view.php?id=804 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [CSE - System and Network Administration]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:IntroductionToMachineLearning CSE302 - Introduction to Machine Learning] [https://moodle.innopolis.university/course/view.php?id=803 (Moodle)]
| colspan="5" style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:StatisticalTechniquesForDataScience CSE310 - Statistical Techniques]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:NatureInspiredComputing CSE340 - Nature Inspired Computing]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:ControlTheory CSE403 - Control Theory] [https://moodle.innopolis.university/course/view.php?id=674 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:IntroductionToRobotics CSE406 - Fundamentals of Robotics]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:TheoreticalMechanics CSE408 - Theoretical Mechanics] [https://moodle.innopolis.university/course/view.php?id=805 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:PhysicsII CSE410 - Physics II] [https://moodle.innopolis.university/course/view.php?id=672 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:Networks CSE501 - Networks] [https://moodle.innopolis.university/course/view.php?id=673 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | Reinforcement Learning
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="font-weight:bold; text-align:center;"
| colspan="7" style="vertical-align:middle; background-color:#EAECF0; color:#202122;" | Y2-T3
|-
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | Subject
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | SD
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | CS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | DS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | AAI
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | R
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | Capstone Project
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/List_of_BSc_Technical_Electives Tech Elective 2 - List of BSc Technical Electives]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/List_of_BSc_Humanitarian_Electives Hum Elective 2 - List of BSc Humanitarian Electives]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | Life Safety
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:PhysicalCultureandSport.S22 HSS105 - Physical Culture and Sport]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="font-weight:bold; text-align:center;"
| colspan="7" style="vertical-align:middle; background-color:#EAECF0; color:#202122;" | Y3-T1
|-
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | Subject
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | SD
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | CS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | DS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | AAI
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | R
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:ProgrammingParadigms CSE109 - Programming Paradigms] [https://moodle.innopolis.university/course/view.php?id=851 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:CompilersConstruction CSE115 — Compiler Construction]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:InformationRetrieval CSE306 - Information Retrieval] [https://moodle.innopolis.university/course/view.php?id=683 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:IntroductionToComputerVision CSE307 - Introduction to Computer Vision] [https://moodle.innopolis.university/course/view.php?id=812 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:PracticalMachineLearningDeepLearning CSE308 - Practical Machine Learning and Deep Learning] [https://moodle.innopolis.university/course/view.php?id=811 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:NaturalLanguageProcessing CSE311 - Natural Language Processing] [https://moodle.innopolis.university/course/view.php?id=851 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:Mechatronics CSE405 - Mechatronics]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:MechanicsAndMachines CSE407 - Mechanics & Machines]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:NetworkAndCyberSecurity CSE503 - Network and Cyber Security] [https://moodle.innopolis.university/course/view.php?id=851 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:FundamentalsofComputerSecurity CSE509 — Fundamentals of Information Security]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:LeanSoftwareDevelopment CSE803 - Lean Software Development] [https://moodle.innopolis.university/course/view.php?id=851 (Moodle)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/List_of_BSc_Technical_Electives Tech Elective 3 - List of BSc Technical Electives]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="font-weight:bold; text-align:center;"
| colspan="7" style="vertical-align:middle; background-color:#EAECF0; color:#202122;" | Y3-T2
|-
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | Subject
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | SD
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | CS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | DS
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | AAI
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122; text-align:center;" | R
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:AdvancedCompilersConstructionandProgramAnalysis CSE108 - Advanced Compilers Construction and Program Analysis]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:IntroductionToBigData CSE303 — Introduction to Big Data]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:GameTheory CSE304 - Game Theory]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:DataMining.S22 CSE305 - Data Mining]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:SensorsAndSensing CSE404 - Sensors & Sensing]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:RoboticSystems CSE409 - Robotic Systems]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:SignalsAndSystems CSE504 — Digital Signal Processing (early Signals And Systems)]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:DevOpsEngineering CSE508 - DevOps Engineering]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:SoftwareQualityandReliability.S22 CSE806 - Software Quality, Reliability and Security]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | Human-AI Interaction Design
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | Explainable and Fair AI
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | Data and Knowledge Representation
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | Autonomous Robotics
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/BSc:_Secure_System_Development Secure System Development]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" |
|- style="text-align:center;"
| style="background-color:#F8F9FA; color:#36B; text-align:left;" | [https://eduwiki.innopolis.university/index.php/List_of_BSc_Technical_Electives Tech Elective 3 - List of BSc Technical Electives]
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes
| style="vertical-align:middle; background-color:#F8F9FA; color:#202122;" | Yes

BSc: Secure System Development

2022-09-19T10:57:18Z

R.akhmetzyanov: /* Short Description */

= Secure development =
* '''Course name''': Secure development
* '''Code discipline''': XXX
* '''Subject area''': Security and Networks

== Short Description ==
The course is aimed to cover security aspects of development. It covers security architecture, secure coding, security assurance, security operation and basic security concepts. We would go from the deepest kernel (ASLR, NX/DEP, CET, KPTI against ROP, UAF, etc) to theoretical high (access models, Biba and Bell-LaPadula model, security principles), from practical design (NIST recommendations and security by design) to day-by-day operations (OSA practices). We would discuss fuzzing, stat analyzers power and SIEM. The course would be extremely useful for security architects. We would discuss not only security, but also safety topics because the mitigations for them are intersecting. The examples would be given based on Linux OS

== Prerequisites ==

=== Prerequisite subjects ===
* CSE101: Introduction to Programming
* CSE112: Software Systems Analysis and Design

=== Prerequisite topics ===
* Basic programming skills, C/C++ is recommended
* Software design or software architecture
* Basics of compilers

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Basics of security ||
# Security and safety. Security and code quality. Maintainability and security. Why it is so hard to develop a secure system and what approaches may be applied? When it makes sense to drive system secure?
|-
| Security architecture ||
# NIST recommendations
# Security principles
# Theoretical security: access matrix and security models
# Secure by design
|-
| Secure coding ||
# Security on the code level
# SDL
# Main binary vulnerabilities and their mitigations
|-
| Secure operating ||
# Security monitoring
# DevSecOps
# Dealing with 3rd parties
|-
| Security assurance ||
# Pen testing
# Fuzzing
# Bug Bounty programs
|-
| Linux security ||
# Keep it all together and see how Linux kernel deals with that.
# SELinux
# GrSec patches
# Why Linux is not safety system
|}
== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to give students a security vision from up to down, because the security principle of weakest link insist that the weakest part of the process/system would be the one to be attacked.

=== ILOs defined at three levels ===

==== Level 1: What concepts should a student know/remember/explain? ====
By the end of the course, the students should be able to ...
* Remember main security principles
* List SDL stages
* Describe the difference between security and safety
* Explain basic binary vulnerabilities
* Specify the required security assurance
* Describe the key elements of SOC systems
* Explain why fuzzing is not the same as unit or integration testing

==== Level 2: What basic practical skills should a student be able to perform? ====
By the end of the course, the students should be able to ...
* Perform Threat Modeling
* Review code to find insecure patterns
* Deal with open source code securely
* Explain the value of bug bounty programme and find the right moment to start it

==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
By the end of the course, the students should be able to ...
* Suggest hardenings and architecture drifts to achieve required level of s&s
* Propose process improvement in a cost-effective manner that would drastically improve the security and safety level.
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 80-100 || -
|-
| B. Good || 60-79 || -
|-
| C. Satisfactory || 40-59 || -
|-
| D. Fail || 0-39 || -
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Assignment/Labs || 70
|-
| Final quiz || 30
|}

=== Recommendations for students on how to succeed in the course ===
Participation is important. Showing up is the key to success in this course. If you don’t have a corresponding technical background, please do not hesitate to ask lecturer. If you feel that the gap is deep, request for extra reading. Reading the recommended literature is optional, and will give you a deeper understanding of the material.

== Resources, literature and reference materials ==

=== Open access resources ===
* Owasp.com
* MITRE SOC Operations https://www.mitre.org/sites/default/files/publications/11-strategies-of-a-world-class-cybersecurity-operations-center.pdf
* MISRA, AUTOSAR, SEI CERT
* https://www.microsoft.com/en-us/securityengineering/sdl

=== Closed access resources ===
* Matt Bishop, (2018) “Computer Security: Art and Science”
* D Deougun, DB Jonhsson, D Sawano (2019) “Secure by design”
* D LeBlanc, Michael Howard (2002) “Writing secure code”

=== Software and tools used within the course ===
* Some static analyser
* AFL
= Teaching Methodology: Methods, techniques, & activities =

== Activities and Teaching Methods ==
{| class="wikitable"
|+ Teaching and Learning Methods within each section
|-
! Teaching Techniques !! Section 1 !! Section 2 !! Section 3 !! Section 4 !! Section 5 !! Section 6
|-
| Problem-based learning (students learn by solving open-ended problems without a strictly-defined solution) || 1 || 1 || 1 || 1 || 1 || 1
|-
| Modular learning (facilitated self-study) || 1 || 1 || 1 || 1 || 1 || 1
|-
| Differentiated learning (provide tasks and activities at several levels of difficulty to fit students needs and level) || 1 || 1 || 1 || 1 || 1 || 1
|-
| Contextual learning (activities and tasks are connected to the real world to make it easier for students to relate to them); || 1 || 1 || 1 || 1 || 1 || 1
|-
| Business game (learn by playing a game that incorporates the principles of the material covered within the course). || 1 || 1 || 1 || 1 || 1 || 1
|}
{| class="wikitable"
|+ Activities within each section
|-
! Learning Activities !! Section 1 !! Section 2 !! Section 3 !! Section 4 !! Section 5 !! Section 6
|-
| Lectures || 1 || 1 || 1 || 1 || 1 || 1
|-
| Lab exercises || 1 || 1 || 1 || 1 || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Individual Assignments || A2: Product Ideation and Market Research Find all weakness in the code snippet. Suggest how to fix them in a secure way. What is your recommendation for the code author? || 1
|}
==== Section 2 ====

==== Section 3 ====

==== Section 4 ====

==== Section 5 ====

==== Section 6 ====

=== Final assessment ===
'''Section 1'''

'''Section 2'''

'''Section 3'''

'''Section 4'''

'''Section 5'''

'''Section 6'''

=== The retake exam ===
'''Section 1'''

'''Section 2'''

'''Section 3'''

'''Section 4'''

'''Section 5'''

'''Section 6'''

MSc: Syllabi Index

2022-09-12T07:11:34Z

R.akhmetzyanov: /* Security and Networks */

= Numbered courses =

== Data Science ==

* CSE132 — Software Design with Python
* [https://eduwiki.innopolis.university/index.php/MSc:_Machine_Learning CSE328 — Machine Learning]
* [https://eduwiki.innopolis.university/index.php/MSc:_Empirical_Methods CSE329 — Empirical Methods]
* [https://eduwiki.innopolis.university/index.php/MSc:_Big_Data_Technologies_And_Analytics CSE330 — Big Data Technologies And Analytics]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Statistics CSE331 — Advanced Statistics]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Machine_Learning CSE332 — Advanced Machine Learning]
* [https://eduwiki.innopolis.university/index.php/MSc:_Optimization CSE333 — Optimization]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Information_Retrieval CSE334 — Advanced Information Retrieval]
* [https://eduwiki.innopolis.university/index.php/MSc:_High-Dimensional_Data_Analysis CSE335 — High-Dimensional Data Analysis]
* [https://eduwiki.innopolis.university/index.php/MSc:_Computer_Vision CSE427 — Computer Vision]

== Robotics ==
* [https://eduwiki.innopolis.university/index.php/MSc:_Fundamentals_of_Robot_Control CSE424 — Fundamental of Robot Control]
* [https://eduwiki.innopolis.university/index.php/MSc:_Sensing_Perception_Actuation CSE425 — Sensing, Perception And Actuation]
* [https://eduwiki.innopolis.university/index.php/MSc:_Dynamics_Of_Non_Linear_Robotic_Systems CSE426 — Dynamics Of Non-Linear Robotic Systems]
* [https://eduwiki.innopolis.university/index.php/MSc:_Computational_Intelligence CSE428 — Computational Intelligence]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Robotics CSE429 — Advanced Robotics]

== Security and Networks ==
* [https://eduwiki.innopolis.university/index.php/MSc:_Computer_Systems_and_Networks Computer Systems and Networks]
* [https://eduwiki.innopolis.university/index.php/MSc:_Classical_Internet_Applications CSE517 — Classical Internet Applications]
* [https://eduwiki.innopolis.university/index.php/MSc:_Inter_Networking_and_Routing CSE518 — Internetworking And Routing]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Networking CSE519 — Advanced Networking]
* [https://eduwiki.innopolis.university/index.php/MSc:_Security_of_systems_and_networks CSE520 — Security of systems and networks]
* [https://eduwiki.innopolis.university/index.php/MSc:_Large_Systems CSE521 — Large Systems]
* [https://eduwiki.innopolis.university/index.php/MSc:_Cybercrime_Forensics CSE523 — Cybercrime And Forensics]
* [https://eduwiki.innopolis.university/index.php/MSc:_Offensive_Technologies CSE524 — Offensive Technologies]
* [https://eduwiki.innopolis.university/index.php/MSc:_Secure_Development CSE533 — Secure Development]

== Software Engineering ==
* [https://eduwiki.innopolis.university/index.php/MSc:_Managing_Software_Development CSE820 — Managing Software Development]
* [https://eduwiki.innopolis.university/index.php/MSc:_Requirements_Engineering CSE821 — Requirements Engineering]
* [https://eduwiki.innopolis.university/index.php/MSc:_Personal_Software_Process CSE822 — Personal Software Process]
* [https://eduwiki.innopolis.university/index.php/MSc:_Models_Software_Systems CSE823 — Models of Software Systems]
* [https://eduwiki.innopolis.university/index.php/MSc:_Architectures_of_Software_Systems CSE824 — Architectures of Software Systems]
* [https://eduwiki.innopolis.university/index.php/MSc:_Analysis_Software_Artifacts CSE825 — Analysis of Software Artifacts]
* [https://eduwiki.innopolis.university/index.php/MSc:_Communication CSE826 — Communication]

== Projects ==
*[https://eduwiki.innopolis.university/index.php/MSc:_Research_Project_SNE CSE525 — Research Project (SNE)]
*[https://eduwiki.innopolis.university/index.php/MSc:_Industrial_SNE_Project CSE526 — Industrial SNE Project]
*[https://eduwiki.innopolis.university/index.php/MSc:_Industrial_Project CSE533 — Industrial Project (MIST-SE)]
*[https://eduwiki.innopolis.university/index.php/MSc:_Master_Practicum_Project CSE602 — Master Practicum Project]
*[https://eduwiki.innopolis.university/index.php/MSc:_Project_R CSE711 — Project R]

== Humanitarian ==
*[https://eduwiki.innopolis.university/index.php/MSc:_SystematicLiteratureReviewForDataScience&Robotics HSS501 — Systematic Literature Review for Data Science & Robotics]
*[https://eduwiki.innopolis.university/index.php/MSc:_Unit-economics_For_IT_startups HSS*** — Unit-economics for IT startups: metrics-based decision making]

MSc: Syllabi Index

2022-09-12T07:11:11Z

R.akhmetzyanov: /* Security and Networks */

= Numbered courses =

== Data Science ==

* CSE132 — Software Design with Python
* [https://eduwiki.innopolis.university/index.php/MSc:_Machine_Learning CSE328 — Machine Learning]
* [https://eduwiki.innopolis.university/index.php/MSc:_Empirical_Methods CSE329 — Empirical Methods]
* [https://eduwiki.innopolis.university/index.php/MSc:_Big_Data_Technologies_And_Analytics CSE330 — Big Data Technologies And Analytics]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Statistics CSE331 — Advanced Statistics]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Machine_Learning CSE332 — Advanced Machine Learning]
* [https://eduwiki.innopolis.university/index.php/MSc:_Optimization CSE333 — Optimization]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Information_Retrieval CSE334 — Advanced Information Retrieval]
* [https://eduwiki.innopolis.university/index.php/MSc:_High-Dimensional_Data_Analysis CSE335 — High-Dimensional Data Analysis]
* [https://eduwiki.innopolis.university/index.php/MSc:_Computer_Vision CSE427 — Computer Vision]

== Robotics ==
* [https://eduwiki.innopolis.university/index.php/MSc:_Fundamentals_of_Robot_Control CSE424 — Fundamental of Robot Control]
* [https://eduwiki.innopolis.university/index.php/MSc:_Sensing_Perception_Actuation CSE425 — Sensing, Perception And Actuation]
* [https://eduwiki.innopolis.university/index.php/MSc:_Dynamics_Of_Non_Linear_Robotic_Systems CSE426 — Dynamics Of Non-Linear Robotic Systems]
* [https://eduwiki.innopolis.university/index.php/MSc:_Computational_Intelligence CSE428 — Computational Intelligence]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Robotics CSE429 — Advanced Robotics]

== Security and Networks ==
* [https://eduwiki.innopolis.university/index.php/MSc:_Computer_Systems_and_Networks Computer Systems and Networks]
* [https://eduwiki.innopolis.university/index.php/MSc:_Classical_Internet_Applications CSE517 — Classical Internet Applications]
* [https://eduwiki.innopolis.university/index.php/MSc:_Inter_Networking_and_Routing CSE518 — Internetworking And Routing]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Networking CSE519 — Advanced Networking]
* [https://eduwiki.innopolis.university/index.php/MSc:_Security_of_systems_and_networks CSE520 — Security of systems and networks]
* [https://eduwiki.innopolis.university/index.php/MSc:_Large_Systems CSE521 — Large Systems]
* [https://eduwiki.innopolis.university/index.php/MSc:_Secure_Development CSE533 — Secure Development]
* [https://eduwiki.innopolis.university/index.php/MSc:_Cybercrime_Forensics CSE523 — Cybercrime And Forensics]
* [https://eduwiki.innopolis.university/index.php/MSc:_Offensive_Technologies CSE524 — Offensive Technologies]

== Software Engineering ==
* [https://eduwiki.innopolis.university/index.php/MSc:_Managing_Software_Development CSE820 — Managing Software Development]
* [https://eduwiki.innopolis.university/index.php/MSc:_Requirements_Engineering CSE821 — Requirements Engineering]
* [https://eduwiki.innopolis.university/index.php/MSc:_Personal_Software_Process CSE822 — Personal Software Process]
* [https://eduwiki.innopolis.university/index.php/MSc:_Models_Software_Systems CSE823 — Models of Software Systems]
* [https://eduwiki.innopolis.university/index.php/MSc:_Architectures_of_Software_Systems CSE824 — Architectures of Software Systems]
* [https://eduwiki.innopolis.university/index.php/MSc:_Analysis_Software_Artifacts CSE825 — Analysis of Software Artifacts]
* [https://eduwiki.innopolis.university/index.php/MSc:_Communication CSE826 — Communication]

== Projects ==
*[https://eduwiki.innopolis.university/index.php/MSc:_Research_Project_SNE CSE525 — Research Project (SNE)]
*[https://eduwiki.innopolis.university/index.php/MSc:_Industrial_SNE_Project CSE526 — Industrial SNE Project]
*[https://eduwiki.innopolis.university/index.php/MSc:_Industrial_Project CSE533 — Industrial Project (MIST-SE)]
*[https://eduwiki.innopolis.university/index.php/MSc:_Master_Practicum_Project CSE602 — Master Practicum Project]
*[https://eduwiki.innopolis.university/index.php/MSc:_Project_R CSE711 — Project R]

== Humanitarian ==
*[https://eduwiki.innopolis.university/index.php/MSc:_SystematicLiteratureReviewForDataScience&Robotics HSS501 — Systematic Literature Review for Data Science & Robotics]
*[https://eduwiki.innopolis.university/index.php/MSc:_Unit-economics_For_IT_startups HSS*** — Unit-economics for IT startups: metrics-based decision making]

ALL:StudyPlan

2022-09-02T06:11:12Z

R.akhmetzyanov: /* Current Study Plans */

== Current Study Plans==

{| class="wikitable"
|- style="font-weight:bold; text-align:center;"
! style="text-align:center;" | Year
| style="text-align:center;"| Computer science
|-

|-
! style="text-align:center;"| 2019-2023
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1KfBHd2fl6ZgKyQ6UXSghPDnGFDtMzwSN/edit?usp=drive_web&ouid=112969423608769094940&rtpof=true BS4]
|-
|-
! style="text-align:center;"| 2020-2024
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1oWdxFeqrDX-4hUsEePOUBc8FAIvLJz5S/edit?usp=drive_web&ouid=112969423608769094940&rtpof=true BS3]
|-
|-
! style="text-align:center;"| 2021-2025
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1JKhIKSayoKIvolPTE4QwT4S4_TmcBloz/edit#gid=102029621 BS2]
|-
|-
! style="text-align:center;"| 2022-2026 DSAI
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/16GSdVMHHbMTq5nAw9uAyL1eD474d_WE-/edit?rtpof=true BS1]
|-
|-
! style="text-align:center;"| 2022-2026 CS
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1UQr75JJBYQjgXBE2nxf82XOjTVs2WWEH/edit#gid=1431294653 BS1]
|-

{| class="wikitable"
|- style="font-weight:bold; text-align:center;"
! style="text-align:center;" | Year
| style="text-align:center;"| DAAI
| style="text-align:center;"| ROCV
| style="text-align:center;"| SE
| style="text-align:center;"| SNE
| style="text-align:center;"| ITE
|-

|-
! style="text-align:center;"| 2021-2023
| style="text-align:center;"| [https://eduwiki.innopolis.university/index.php/MS1:DAAI:StudyPlan MS2]
| style="text-align:center;"| [https://eduwiki.innopolis.university/index.php/MS1:ROCV:StudyPlan MS2]
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1wsIL7VVfHmANr684t1xDJxwuPFYLY1JZ/edit?rtpof=true#gid=1323274448 MS2]
| style="text-align:center;"| [https://eduwiki.innopolis.university/index.php/MS1:SNE:StudyPlan MS2]
|-
|-
! style="text-align:center;"| 2022-2024
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1MHowXuL3RFfuFJnXaJttYtL0Td-qauXb/edit?usp=drive_web&ouid=112969423608769094940&rtpof=true:DAAI:StudyPlan MS1]
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/11ccfxuNrQuyq-V3niO_SrVotlJ2J3E_q/edit?usp=drive_web&ouid=112969423608769094940&rtpof=true:ROCV:StudyPlan MS1]
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1zNiCqdfepVb6I-sLZIUEA9liuDuoojT2/edit?rtpof=true MS1 SE]
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/199swG0cHnskzSGOEXHvD50ZokuB5U3V3/edit?usp=drive_web&ouid=112969423608769094940&rtpof=true:SNE:StudyPlan MS1]
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1-AVsKCXr3tHivLrb7L7rUwY84F34Z7r3/edit?usp=drive_web&ouid=112969423608769094940&rtpof=true:ITE:StudyPlan MS1]
|-

ALL:StudyPlan

2022-09-02T06:09:55Z

R.akhmetzyanov: /* Current Study Plans */

== Current Study Plans==

{| class="wikitable"
|- style="font-weight:bold; text-align:center;"
! style="text-align:center;" | Year
| style="text-align:center;"| Computer science
|-

|-
! style="text-align:center;"| 2019-2023
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1KfBHd2fl6ZgKyQ6UXSghPDnGFDtMzwSN/edit?usp=drive_web&ouid=112969423608769094940&rtpof=true BS4]
|-
|-
! style="text-align:center;"| 2020-2024
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1oWdxFeqrDX-4hUsEePOUBc8FAIvLJz5S/edit?usp=drive_web&ouid=112969423608769094940&rtpof=true BS3]
|-
|-
! style="text-align:center;"| 2021-2025
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1JKhIKSayoKIvolPTE4QwT4S4_TmcBloz/edit#gid=102029621 BS2]
|-
|-
! style="text-align:center;"| 2022-2026 DSAI
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/16GSdVMHHbMTq5nAw9uAyL1eD474d_WE-/edit?rtpof=true BS1]
|-
|-
! style="text-align:center;"| 2022-2026 CS
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1UQr75JJBYQjgXBE2nxf82XOjTVs2WWEH/edit#gid=1431294653 BS1]
|-

{| class="wikitable"
|- style="font-weight:bold; text-align:center;"
! style="text-align:center;" | Year
| style="text-align:center;"| DAAI
| style="text-align:center;"| ROCV
| style="text-align:center;"| SE
| style="text-align:center;"| SNE
|-

|-
! style="text-align:center;"| 2021-2023
| style="text-align:center;"| [https://eduwiki.innopolis.university/index.php/MS1:DAAI:StudyPlan MS2]
| style="text-align:center;"| [https://eduwiki.innopolis.university/index.php/MS1:ROCV:StudyPlan MS2]
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1wsIL7VVfHmANr684t1xDJxwuPFYLY1JZ/edit?rtpof=true#gid=1323274448 MS2]
| style="text-align:center;"| [https://eduwiki.innopolis.university/index.php/MS1:SNE:StudyPlan MS2]
|-
|-
! style="text-align:center;"| 2022-2024
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1MHowXuL3RFfuFJnXaJttYtL0Td-qauXb/edit?usp=drive_web&ouid=112969423608769094940&rtpof=true:DAAI:StudyPlan MS1]
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/11ccfxuNrQuyq-V3niO_SrVotlJ2J3E_q/edit?usp=drive_web&ouid=112969423608769094940&rtpof=true:ROCV:StudyPlan MS1]
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1zNiCqdfepVb6I-sLZIUEA9liuDuoojT2/edit?rtpof=true MS1 SE]
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/199swG0cHnskzSGOEXHvD50ZokuB5U3V3/edit?usp=drive_web&ouid=112969423608769094940&rtpof=true:SNE:StudyPlan MS1]
| style="text-align:center;"| [https://docs.google.com/spreadsheets/d/1-AVsKCXr3tHivLrb7L7rUwY84F34Z7r3/edit?usp=drive_web&ouid=112969423608769094940&rtpof=true:ITE:StudyPlan MS1] ITE
|-

BS1

2022-08-29T11:43:05Z

R.akhmetzyanov:

<googlespreadsheet width="1600" height="1000" style="width: 100%">1rzxIO7oFgFBauALaiCHZwza8sDg93EKwKMzzAb22_XE/edit#gid=833790814 </googlespreadsheet>

MSc: Cybercrime Forensics

2022-08-29T10:58:51Z

R.akhmetzyanov:

= Cybercrime and Forensics =

* Course name: Cybercrime and Forensics
* Course number: SNE-???

== Course characteristics ==

== Key concepts of the class ==

* Law, regulations and modern tendencies of the high-tech crimes
* Computer forensics approaches and techniques
* Incident response and threat hunting methods

== What is the purpose of this course? ==

Modern tactics and techniques of high-tech crimes, including counter -forensics methods, are evolving rapidly according to the past several years. Therefore, the purpose of this course is to provide for students the necessary knowledge and abilities to obtain and analyze digital evidence in a way to provide investigations that will comply with the current law and regulations. Another purpose for the course is to learn for students how to counteract with ongoing computer incidents, intrusions and to perform threat hunting in the computer systems

== Prerequisites ==
The course has been designed to be self-included as much as possible. The successful completion will depend on prerequisite courses such as:
* [https://eduwiki.innopolis.university/index.php/MSc:_Security_of_systems_and_networks CSE520 — Security of systems and networks]
* Essential skills
* Classical Internet Applications

== Course Objectives Based on Bloom’s Taxonomy ==

=== What should a student remember at the end of the course? ===

By the end of the course, the students should be able to identify and define

* Methods for investigating and responding to cybersecurity incidents
* Main types of computer attacks and the technical and non-technical techniques used by attackers;
* Compliance requirements to produce valid computer-technical expertise for further legal procedures;
* Aquisition techniques depending on the affected digital media and environment conditions
* Computer systems’ artifacts that were affected during the incident
* Specific hardware and software forensic tools depending on the type of incident;
* Decryption and decoding methods for protected and hidden data, methods of counter-forensics technology.

=== What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to describe and explain

* Difference between different types of computer incidents
* The difference in compliance requirements for specific cybercrime cases
* Computer attacker model and kill chain tactics
* Filesystems analysis methods
* Volatile memory analysis methods
* Network analysis methods
* Malware analysis methods

=== What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to demonstrate

* Organizing an incident response to a cybersecurity incident and minimize potential damage
* Determination of the type and causes of the incident
* Determination of the computer systems’ artifacts that are required for the acquisition
* Collection of digital evidence and proper documentation of it
* Recovered deleted and hidden information
* Restored an incident chronology during the investigation, determination of the methods used by the attacker and the impact on the attacked system
* Conduction of investigation on various types of computer attacks
* Conduction malware analysis
* Correct and efficient usage of open source forensics software and hardware

== Course evaluation ==

{|
|+ Course grade breakdown
!align="center"| '''Type'''
!align="center"| '''Default points'''
!align="center"| '''Proposed points'''
|-
|align="center"| Labs/seminar classes
|align="center"| 20
|align="center"| 20
|-
|align="center"| Project
|align="center"| 30
|align="center"| 60
|-
|align="center"| Exam
|align="center"| 50
|align="center"| 20
|}

If necessary, please indicate freely your course’s features in terms of students’ performance assessment: None

== Grades range ==

{|
|+ Course grading range
!align="center"| '''Grade'''
!align="center"| '''Default range'''
!align="center"| '''Proposed range'''
|-
|align="center"| A. Excellent
|align="center"| 90-100
|align="center"| 90-100
|-
|align="center"| B. Good
|align="center"| 75-89
|align="center"| 70-89
|-
|align="center"| C. Satisfactory
|align="center"| 60-74
|align="center"| 60-69
|-
|align="center"| D. Poor
|align="center"| 0-59
|align="center"| 0-59
|}

If necessary, please indicate freely your course’s grading features:

The laboratory assignments are mandatory with a required minimum result of 6/10 for each - including re-takes and late submissions - to complete the course. The semester starts with the default range as proposed in the Table above, but it may changes slightly depending on how the semester progresses.

== Resources and reference material ==

* “Practical forensic imaging. Securing digital evidence with Linux tools”. Bruce Nikkel
* “Incident response and computer forensics”. K.Mandia, C.Prosise, and M.Pepe
* “Digital Evidence and Computer Crime”. Eoghan Casey

== Course Sections ==

{|
|+ Course Sections
!align="center"| '''Section'''
!align="center"| '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
|align="center"| Modern high-tech crimes and the law
|align="center"| 4
|-
|align="center"| 2
|align="center"| Data acquisition and securing digital evidence
|align="center"| 4
|-
|align="center"| 3
|align="center"| Computer systems’ artifacts and their analysis methods
|align="center"| 6
|-
|align="center"| 4
|align="center"| Volatile data analysis methods
|align="center"| 6
|-
|align="center"| 5
|align="center"| Incident response and threat hunting
|align="center"| 4
|-
|align="center"| 6
|align="center"| Labs
|align="center"| 56
|}

== Section 1 ==

'''Section title:''' Modern high-tech crimes and the law

'''Topics covered in this section:'''

* Law, regulations and modern tendencies of the high-tech crimes
* Computer forensics approaches and techniques
* Incident response and threat hunting methods

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What are the typical attacks which can be used against the banking system?
* What is the attacker model?
* What is the computer incident?
* What types of incidents can lead to criminal code articles for an attacker?

'''Typical questions for seminar classes (labs) within this section'''

* Identify risks and develop mitigation techniques before acquiring evidence for a given case
* Develop an attacker model for a specific incident
* Identify the most important compliance requirements for preservation evidence in the court case?

'''Test questions for final assessment in this section'''

* As above

== Section 2 ==

'''Section title:''' Data acquisition and securing digital evidence

'''Topics covered in this section:'''

* Compliance requirements for the evidence acquisition
* Non-volatile data evidence collection
* Volatile data evidence collection
* Securing digital evidence with open source tools

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What are the pros and cons of using software or hardware tools for acquisition?
* What are the important steps to perform data evidence acquisition on the live system?
* What is the difference between non-volatile and volatile data from the perspective of computer forensics?
* What are the legal aspects of preparing before conducting computer forensic analysis based on the positions and responsibilities of forensic investigators?
* What kind of computer systems’ components would be less important during a live acquisition?

'''Typical questions for seminar classes (labs) within this section'''

* Depending on the incident define software and hardware that can be used to collect and preserve digital evidence
* Collect the evidence on a virtual environment
* Collect the evidence from the live system
* Collect the evidence of the volatile data
* Provide integrity, confidentiality, and non-repudiation for acquired evidence

'''Test questions for final assessment in this section'''

As above

== Section 3 ==

'''Section title:'''

'''Topics covered in this section:''' Computer systems’ artifacts and their analysis methods

* Anti-forensics methods and recovery information
* Windows forensics
* Filesystem forensics

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What kind of methods do you know for an attacker to hide and delete information?
* What are the important artifacts that can be used for the analysis of the Windows systems?
* What is the difference between DEFT and CAIN software forensics distributions?
* What is MAC time?
* What is the conceptual difference between FAT and NTFS?

'''Typical questions for seminar classes (labs) within this section'''

* Analyze the incident that involves the USB stick of the attacker
* Create a timeline based on the timestamps of the artifacts
* Find and recover hidden information on the hard drive
* Extract and analyze filesystem journals
* Find encrypted information
* Identify the slack spaces that contain deleted data

'''Test questions for final assessment in this section'''

As above

== Section 4 ==

'''Section title:''' Volatile data analysis methods

'''Topics covered in this section:'''

* Operating memory forensics
* Network forensics

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What is fileless malware?
* How can rootkits affect the evidence?
* What kind of operating memory artifacts can be useful for cybercrime investigation?
* What is difficult about dumping a memory?
* What is difficult about dumping network traffic?

'''Typical questions for seminar classes (labs) within this section'''

* Identify direct kernel object manipulation in the given sample
* Find unlinking from the active process list
* Trace and detect used cryptographical keys on the incident
* Determine the original source of an attacker’s compromise on the given network traffic
* Establish and present a timeline of the attacker’s activities for a specific case

'''Test questions for final assessment in this section'''

As above

== Section 5 ==

'''Section title:''' Incident response and threat hunting

'''Topics covered in this section:'''

* Introduction to incident response
* Sandboxing
* Malware analysis
* SOC analysis tasks
* Monitoring, logging and auditing of security events

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What limitations might you have during the incident response?
* What type of incident responses can be provided during the incident
* What is the difference between incident response and computer forensics in general
* What is sandboxing and how it could be used in the incident response?
* What type of threats can occur for investigators during investigation?

'''Typical questions for seminar classes (labs) within this section'''

* Identify the methods that can detect anomaly behavior for a typical Windows system processes
* Identify persistence mechanisms that are used by the given malicious process
* Identify illegitimate network activity on the given network traffic
* Develop an effective sandboxing environment for malware detection and examination of its behavior
* Develop indicators of compromise to detect threats on multiple systems

'''Test questions for final assessment in this section'''

As above

MSc: Cybercrime Forensics

2022-08-29T09:37:53Z

R.akhmetzyanov:

= Cybercrime and Forensics =
* Course name: Cybercrime and Forensics
* Course number:
* Subject area:

== Short Description ==
Modern tactics and techniques of high-tech crimes, including counter -forensics methods, are evolving rapidly according to the past several years. Therefore, the purpose of this course is to provide for students the necessary knowledge and abilities to obtain and analyze digital evidence in a way to provide investigations that will comply with the current law and regulations. Another purpose for the course is to learn for students how to counteract with ongoing computer incidents, intrusions and to perform threat hunting in the computer systems

== Prerequisites ==

=== Prerequisite subjects ===

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Booting, Operating Systems, and Computer Architecture ||
# Booting principles and disks
# Essentials of operating systems
# Fundamentals of computer architecture
|-
| DNS, DNSSEC, and DoH ||
# DNS
# DNSSEC
# DoH
|-
| Email ||
# Email architecture
# Spam management
|-
| Directory, Web, Protocol, ABNF and Deflating ||
# Directory services
# Web
# Protocols
# ABNF
# Deflating
|}
== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
CIA course serves as kick-start for the security and network engineering Masters program. Before diving into the depth of the topics, the students must know preliminary concepts related to computer networks services and applications therein. This course is designed to cover the basic services offered by the Internet including operating systems and computer architecture. The concepts from this course will be used throughout the course of whole masters. More precisely, this course will cover the basic computer architecture and assembly language programming, Domain Name Services (DNS), DNSSec, email, web, directories, and disks. This course will also cover protocols and ABNF. The theory part will strengthen the theoretical aspects of the concepts whereas the lab exercises will provide the students with the opportunity to have hands-on experience of the ideas they learnt in the lectures.

=== ILOs defined at three levels ===

==== Level 1: What concepts should a student know/remember/explain? ====
By the end of the course, the students should be able to ...
* Identify different Internet applications and understand their working principles from the protocols point of view
* Demonstrate the acquired knowledge and skills in classical internet applications including DNS, Email, and Directory services.
* Able to write regular expressions and context-free grammar that are essential in Internet applications and information exchange through the networks
* Able to partition disks and remember the booting principles as well as secure booting

==== Level 2: What basic practical skills should a student be able to perform? ====
By the end of the course, the students should be able to ...
* Demonstrate knowledge and skills to use web services
* Demonstrate the essential knowledge of disks and calculate particular locations/addresses in disks
* Reason about problems in the current DNS and the need to upgrade to DNSSEC and DNS over HTTPS
* Demonstrate the knowledge of email and other services configuration

==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
By the end of the course, the students should be able to ...
* Install, Configure, update, and manage DNS services over a network
* Configure, maintain, and update the secure DNS over a network
* Update, add, and delete records in DNS
* Configure a secure mail server and maintain it
* Get hands-on experience of the afore-mentioned technologies on their own servers.
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 90-100 || -
|-
| B. Good || 75-89 || -
|-
| C. Satisfactory || 60-74 || -
|-
| D. Poor || 0-59 || -
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Labs/seminar classes || 20
|-
| Lab tasks assessment || 40
|-
| Exams || 40
|}

=== Recommendations for students on how to succeed in the course ===

== Resources, literature and reference materials ==

=== Open access resources ===
* Lecture slides
* RFCs
* Link to the online material will be provided (if any)

=== Closed access resources ===

=== Software and tools used within the course ===

= Cybercrime and Forensics =

* Course name: Cybercrime and Forensics
* Course number: SNE-???

== Course characteristics ==

== Key concepts of the class ==

* Law, regulations and modern tendencies of the high-tech crimes
* Computer forensics approaches and techniques
* Incident response and threat hunting methods

== What is the purpose of this course? ==

Modern tactics and techniques of high-tech crimes, including counter -forensics methods, are evolving rapidly according to the past several years. Therefore, the purpose of this course is to provide for students the necessary knowledge and abilities to obtain and analyze digital evidence in a way to provide investigations that will comply with the current law and regulations. Another purpose for the course is to learn for students how to counteract with ongoing computer incidents, intrusions and to perform threat hunting in the computer systems

== Prerequisites ==
The course has been designed to be self-included as much as possible. The successful completion will depend on prerequisite courses such as:
* [https://eduwiki.innopolis.university/index.php/MSc:_Security_of_systems_and_networks CSE520 — Security of systems and networks]
* Essential skills
* Classical Internet Applications

== Course Objectives Based on Bloom’s Taxonomy ==

=== What should a student remember at the end of the course? ===

By the end of the course, the students should be able to identify and define

* Methods for investigating and responding to cybersecurity incidents
* Main types of computer attacks and the technical and non-technical techniques used by attackers;
* Compliance requirements to produce valid computer-technical expertise for further legal procedures;
* Aquisition techniques depending on the affected digital media and environment conditions
* Computer systems’ artifacts that were affected during the incident
* Specific hardware and software forensic tools depending on the type of incident;
* Decryption and decoding methods for protected and hidden data, methods of counter-forensics technology.

=== What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to describe and explain

* Difference between different types of computer incidents
* The difference in compliance requirements for specific cybercrime cases
* Computer attacker model and kill chain tactics
* Filesystems analysis methods
* Volatile memory analysis methods
* Network analysis methods
* Malware analysis methods

=== What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to demonstrate

* Organizing an incident response to a cybersecurity incident and minimize potential damage
* Determination of the type and causes of the incident
* Determination of the computer systems’ artifacts that are required for the acquisition
* Collection of digital evidence and proper documentation of it
* Recovered deleted and hidden information
* Restored an incident chronology during the investigation, determination of the methods used by the attacker and the impact on the attacked system
* Conduction of investigation on various types of computer attacks
* Conduction malware analysis
* Correct and efficient usage of open source forensics software and hardware

== Course evaluation ==

{|
|+ Course grade breakdown
!align="center"| '''Type'''
!align="center"| '''Default points'''
!align="center"| '''Proposed points'''
|-
|align="center"| Labs/seminar classes
|align="center"| 20
|align="center"| 20
|-
|align="center"| Project
|align="center"| 30
|align="center"| 60
|-
|align="center"| Exam
|align="center"| 50
|align="center"| 20
|}

If necessary, please indicate freely your course’s features in terms of students’ performance assessment: None

== Grades range ==

{|
|+ Course grading range
!align="center"| '''Grade'''
!align="center"| '''Default range'''
!align="center"| '''Proposed range'''
|-
|align="center"| A. Excellent
|align="center"| 90-100
|align="center"| 90-100
|-
|align="center"| B. Good
|align="center"| 75-89
|align="center"| 70-89
|-
|align="center"| C. Satisfactory
|align="center"| 60-74
|align="center"| 60-69
|-
|align="center"| D. Poor
|align="center"| 0-59
|align="center"| 0-59
|}

If necessary, please indicate freely your course’s grading features:

The laboratory assignments are mandatory with a required minimum result of 6/10 for each - including re-takes and late submissions - to complete the course. The semester starts with the default range as proposed in the Table above, but it may changes slightly depending on how the semester progresses.

== Resources and reference material ==

* “Practical forensic imaging. Securing digital evidence with Linux tools”. Bruce Nikkel
* “Incident response and computer forensics”. K.Mandia, C.Prosise, and M.Pepe
* “Digital Evidence and Computer Crime”. Eoghan Casey

== Course Sections ==

{|
|+ Course Sections
!align="center"| '''Section'''
!align="center"| '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
|align="center"| Modern high-tech crimes and the law
|align="center"| 4
|-
|align="center"| 2
|align="center"| Data acquisition and securing digital evidence
|align="center"| 4
|-
|align="center"| 3
|align="center"| Computer systems’ artifacts and their analysis methods
|align="center"| 6
|-
|align="center"| 4
|align="center"| Volatile data analysis methods
|align="center"| 6
|-
|align="center"| 5
|align="center"| Incident response and threat hunting
|align="center"| 4
|-
|align="center"| 6
|align="center"| Labs
|align="center"| 56
|}

== Section 1 ==

'''Section title:''' Modern high-tech crimes and the law

'''Topics covered in this section:'''

* Law, regulations and modern tendencies of the high-tech crimes
* Computer forensics approaches and techniques
* Incident response and threat hunting methods

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What are the typical attacks which can be used against the banking system?
* What is the attacker model?
* What is the computer incident?
* What types of incidents can lead to criminal code articles for an attacker?

'''Typical questions for seminar classes (labs) within this section'''

* Identify risks and develop mitigation techniques before acquiring evidence for a given case
* Develop an attacker model for a specific incident
* Identify the most important compliance requirements for preservation evidence in the court case?

'''Test questions for final assessment in this section'''

* As above

== Section 2 ==

'''Section title:''' Data acquisition and securing digital evidence

'''Topics covered in this section:'''

* Compliance requirements for the evidence acquisition
* Non-volatile data evidence collection
* Volatile data evidence collection
* Securing digital evidence with open source tools

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What are the pros and cons of using software or hardware tools for acquisition?
* What are the important steps to perform data evidence acquisition on the live system?
* What is the difference between non-volatile and volatile data from the perspective of computer forensics?
* What are the legal aspects of preparing before conducting computer forensic analysis based on the positions and responsibilities of forensic investigators?
* What kind of computer systems’ components would be less important during a live acquisition?

'''Typical questions for seminar classes (labs) within this section'''

* Depending on the incident define software and hardware that can be used to collect and preserve digital evidence
* Collect the evidence on a virtual environment
* Collect the evidence from the live system
* Collect the evidence of the volatile data
* Provide integrity, confidentiality, and non-repudiation for acquired evidence

'''Test questions for final assessment in this section'''

As above

== Section 3 ==

'''Section title:'''

'''Topics covered in this section:''' Computer systems’ artifacts and their analysis methods

* Anti-forensics methods and recovery information
* Windows forensics
* Filesystem forensics

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What kind of methods do you know for an attacker to hide and delete information?
* What are the important artifacts that can be used for the analysis of the Windows systems?
* What is the difference between DEFT and CAIN software forensics distributions?
* What is MAC time?
* What is the conceptual difference between FAT and NTFS?

'''Typical questions for seminar classes (labs) within this section'''

* Analyze the incident that involves the USB stick of the attacker
* Create a timeline based on the timestamps of the artifacts
* Find and recover hidden information on the hard drive
* Extract and analyze filesystem journals
* Find encrypted information
* Identify the slack spaces that contain deleted data

'''Test questions for final assessment in this section'''

As above

== Section 4 ==

'''Section title:''' Volatile data analysis methods

'''Topics covered in this section:'''

* Operating memory forensics
* Network forensics

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What is fileless malware?
* How can rootkits affect the evidence?
* What kind of operating memory artifacts can be useful for cybercrime investigation?
* What is difficult about dumping a memory?
* What is difficult about dumping network traffic?

'''Typical questions for seminar classes (labs) within this section'''

* Identify direct kernel object manipulation in the given sample
* Find unlinking from the active process list
* Trace and detect used cryptographical keys on the incident
* Determine the original source of an attacker’s compromise on the given network traffic
* Establish and present a timeline of the attacker’s activities for a specific case

'''Test questions for final assessment in this section'''

As above

== Section 5 ==

'''Section title:''' Incident response and threat hunting

'''Topics covered in this section:'''

* Introduction to incident response
* Sandboxing
* Malware analysis
* SOC analysis tasks
* Monitoring, logging and auditing of security events

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What limitations might you have during the incident response?
* What type of incident responses can be provided during the incident
* What is the difference between incident response and computer forensics in general
* What is sandboxing and how it could be used in the incident response?
* What type of threats can occur for investigators during investigation?

'''Typical questions for seminar classes (labs) within this section'''

* Identify the methods that can detect anomaly behavior for a typical Windows system processes
* Identify persistence mechanisms that are used by the given malicious process
* Identify illegitimate network activity on the given network traffic
* Develop an effective sandboxing environment for malware detection and examination of its behavior
* Develop indicators of compromise to detect threats on multiple systems

'''Test questions for final assessment in this section'''

As above

BSc: Introduction To Programming

2022-08-12T11:39:45Z

R.akhmetzyanov: /* The retake exam */

BSc:Syllabi Index

2022-07-21T14:02:09Z

R.akhmetzyanov: /* Technical */

== Technical ==

=== Computer Science, Programming ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming CSE101 — Introduction to Programming I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming_II CSE102 — Introduction to Programming II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Computer_Science CSE103 — Theoretical Computer Science]
* [https://eduwiki.innopolis.university/index.php/BSc:_Operating_Systems CSE105 — Operating Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Databases CSE106 — Databases]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Compilers_Construction_and_Program_Analysis CSE108 — Advanced Compilers Construction and Program Analysis]
* [https://eduwiki.innopolis.university/index.php/BSc:_Programming_Paradigms CSE109 — Programming Paradigms]
* [https://eduwiki.innopolis.university/index.php/BSc:_Software_Systems_Design CSE112 — Software Systems Analysis and Design]
* [https://eduwiki.innopolis.university/index.php/BSc:_Distributed_And_Network_Programming CSE114 — Distributed And Network Programming]
* [https://eduwiki.innopolis.university/index.php/BSc:_Compilers_Construction CSE115 — Compilers Construction]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Structures_Algorithms CSE117 — Data Structures and Algorithms]

=== Maths ===
* [https://eduwiki.innopolis.university/index.php/BSc:Logic_and_Discrete_Mathematics.f22 CSE113 — Philosophy I - (Discrete Math and Logic)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I.f22 CSE201 — Mathematical Analysis I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I.f22 CSE202 — Analytical Geometry and Linear Algebra I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II.s23 CSE203 — Mathematical Analysis II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II.s23 CSE204 — Analytic Geometry And Linear Algebra II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Differential_Equations.f22 CSE205 — Differential Equations]
* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics.f22 CSE206 — Probability And Statistics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_to_Optimization.F22 CSE??? — Introduction to Optimization]

=== AI ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Artificial_Intelligence CSE301 — Introduction to Artificial Intelligence]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Machine_Learning CSE302 — Introduction to Machine Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Big_Data CSE303 — Introduction to Big Data]
* [https://eduwiki.innopolis.university/index.php/BSc:_Game_Theory CSE304 — Game Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Mining CSE305 — Data Mining]
* [https://eduwiki.innopolis.university/index.php/BSc:_Information_Retrieval CSE306 — Information Retrieval]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Computer_Vision CSE307 — Introduction to Computer Vision]
* [https://eduwiki.innopolis.university/index.php/BSc:_Practical_Machine_Learning_Deep_Learning CSE308 — Practical Machine Learning And Deep Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Statistical_Techniques_For_Data_Science CSE310 — Statistical Techniques for Data Science and Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Natural_Language_Processing CSE311 — Natural Language Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Nature_Inspired_Computing CSE340 — Nature Inspired Computing]
----

=== Hardware and Robotics ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Computer_Architecture CSE401 — Fundamentals of Computer Architecture]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_I CSE402 — Physics I (Mechanics)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Control_Theory CSE403 — Control Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Sensors_And_Sensing CSE404 — Sensors and Sensing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechatronics CSE405 — Mechatronics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Robotics CSE406 — Fundamentals of Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechanics_And_Machines CSE407 — Mechanics And Machines]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Mechanics CSE408 — Theoretical Mechanics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Robotic_Systems CSE409 — Robotic Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_II CSE410 — Physics II - Electrical Engineering]

----

=== Security and Networks ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Networks CSE501 — Networks]
* [https://eduwiki.innopolis.university/index.php/BSc:_System_And_Network_Administration CSE502 — System And Network Administration]
* [https://eduwiki.innopolis.university/index.php/BSc:_Network_And_Cyber_Security CSE503 — Network And Cyber Security]
* [https://eduwiki.innopolis.university/index.php/BSc:_Signals_And_Systems CSE504 — Digital Signal Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Dev_Ops_Engineering CSE508 — DevOps Engineering]
* [https://eduwiki.innopolis.university/index.php/BSc:_Fundamentals_of_Computer_Security CSE509 — Fundamentals of Information Security]

=== SE ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Lean_Software_Development CSE803 — Lean Software Development]
* [https://eduwiki.innopolis.university/index.php/BSc:SoftwareQualityandReliability.S22 CSE806 — Software Quality and Reliability]
* [https://eduwiki.innopolis.university/index.php/Bc:_It_Product_Development CSE807 — IT Product Development]

== Humanities ==

=== Pedagogy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_History HSS601 — History (History of Russia, World History)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physical_Culture_and_Sport HSS105 — Physical Culture and Sport]

=== Languages and literature ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture HSS203 — Foreign Language I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS204 — Foreign Language II]

=== The science ===
* [https://eduwiki.innopolis.university/index.php/BSc:AcademicResearchandWritingCulture HSS501 — Academic Research and Writing Culture 1]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS502 — Academic Research and Writing Culture 2]

=== History and philosophy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Philosophy_II HSS602 — Philosophy II (Languages and Perceptions)]

----

BSc:Syllabi Index

2022-07-21T14:01:53Z

R.akhmetzyanov: /* Computer Science, Programming */

== Technical ==

=== Computer Science, Programming ===

I
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming CSE101 — Introduction to Programming I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming_II CSE102 — Introduction to Programming II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Computer_Science CSE103 — Theoretical Computer Science]
* [https://eduwiki.innopolis.university/index.php/BSc:_Operating_Systems CSE105 — Operating Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Databases CSE106 — Databases]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Compilers_Construction_and_Program_Analysis CSE108 — Advanced Compilers Construction and Program Analysis]
* [https://eduwiki.innopolis.university/index.php/BSc:_Programming_Paradigms CSE109 — Programming Paradigms]
* [https://eduwiki.innopolis.university/index.php/BSc:_Software_Systems_Design CSE112 — Software Systems Analysis and Design]
* [https://eduwiki.innopolis.university/index.php/BSc:_Distributed_And_Network_Programming CSE114 — Distributed And Network Programming]
* [https://eduwiki.innopolis.university/index.php/BSc:_Compilers_Construction CSE115 — Compilers Construction]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Structures_Algorithms CSE117 — Data Structures and Algorithms]

=== Maths ===
* [https://eduwiki.innopolis.university/index.php/BSc:Logic_and_Discrete_Mathematics.f22 CSE113 — Philosophy I - (Discrete Math and Logic)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I.f22 CSE201 — Mathematical Analysis I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I.f22 CSE202 — Analytical Geometry and Linear Algebra I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II.s23 CSE203 — Mathematical Analysis II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II.s23 CSE204 — Analytic Geometry And Linear Algebra II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Differential_Equations.f22 CSE205 — Differential Equations]
* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics.f22 CSE206 — Probability And Statistics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_to_Optimization.F22 CSE??? — Introduction to Optimization]

=== AI ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Artificial_Intelligence CSE301 — Introduction to Artificial Intelligence]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Machine_Learning CSE302 — Introduction to Machine Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Big_Data CSE303 — Introduction to Big Data]
* [https://eduwiki.innopolis.university/index.php/BSc:_Game_Theory CSE304 — Game Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Mining CSE305 — Data Mining]
* [https://eduwiki.innopolis.university/index.php/BSc:_Information_Retrieval CSE306 — Information Retrieval]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Computer_Vision CSE307 — Introduction to Computer Vision]
* [https://eduwiki.innopolis.university/index.php/BSc:_Practical_Machine_Learning_Deep_Learning CSE308 — Practical Machine Learning And Deep Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Statistical_Techniques_For_Data_Science CSE310 — Statistical Techniques for Data Science and Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Natural_Language_Processing CSE311 — Natural Language Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Nature_Inspired_Computing CSE340 — Nature Inspired Computing]
----

=== Hardware and Robotics ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Computer_Architecture CSE401 — Fundamentals of Computer Architecture]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_I CSE402 — Physics I (Mechanics)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Control_Theory CSE403 — Control Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Sensors_And_Sensing CSE404 — Sensors and Sensing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechatronics CSE405 — Mechatronics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Robotics CSE406 — Fundamentals of Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechanics_And_Machines CSE407 — Mechanics And Machines]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Mechanics CSE408 — Theoretical Mechanics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Robotic_Systems CSE409 — Robotic Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_II CSE410 — Physics II - Electrical Engineering]

----

=== Security and Networks ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Networks CSE501 — Networks]
* [https://eduwiki.innopolis.university/index.php/BSc:_System_And_Network_Administration CSE502 — System And Network Administration]
* [https://eduwiki.innopolis.university/index.php/BSc:_Network_And_Cyber_Security CSE503 — Network And Cyber Security]
* [https://eduwiki.innopolis.university/index.php/BSc:_Signals_And_Systems CSE504 — Digital Signal Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Dev_Ops_Engineering CSE508 — DevOps Engineering]
* [https://eduwiki.innopolis.university/index.php/BSc:_Fundamentals_of_Computer_Security CSE509 — Fundamentals of Information Security]

=== SE ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Lean_Software_Development CSE803 — Lean Software Development]
* [https://eduwiki.innopolis.university/index.php/BSc:SoftwareQualityandReliability.S22 CSE806 — Software Quality and Reliability]
* [https://eduwiki.innopolis.university/index.php/Bc:_It_Product_Development CSE807 — IT Product Development]

== Humanities ==

=== Pedagogy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_History HSS601 — History (History of Russia, World History)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physical_Culture_and_Sport HSS105 — Physical Culture and Sport]

=== Languages and literature ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture HSS203 — Foreign Language I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS204 — Foreign Language II]

=== The science ===
* [https://eduwiki.innopolis.university/index.php/BSc:AcademicResearchandWritingCulture HSS501 — Academic Research and Writing Culture 1]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS502 — Academic Research and Writing Culture 2]

=== History and philosophy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Philosophy_II HSS602 — Philosophy II (Languages and Perceptions)]

----

BSc:Syllabi Index

2022-07-21T13:59:57Z

R.akhmetzyanov: /* Technical */

== Technical ==

=== Computer Science, Programming ===


* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming CSE101 — Introduction to Programming I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming_II CSE102 — Introduction to Programming II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Computer_Science CSE103 — Theoretical Computer Science]
* [https://eduwiki.innopolis.university/index.php/BSc:_Operating_Systems CSE105 — Operating Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Databases CSE106 — Databases]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Compilers_Construction_and_Program_Analysis CSE108 — Advanced Compilers Construction and Program Analysis]
* [https://eduwiki.innopolis.university/index.php/BSc:_Programming_Paradigms CSE109 — Programming Paradigms]
* [https://eduwiki.innopolis.university/index.php/BSc:_Software_Systems_Design CSE112 — Software Systems Analysis and Design]
* [https://eduwiki.innopolis.university/index.php/BSc:_Distributed_And_Network_Programming CSE114 — Distributed And Network Programming]
* [https://eduwiki.innopolis.university/index.php/BSc:_Compilers_Construction CSE115 — Compilers Construction]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Structures_Algorithms CSE117 — Data Structures and Algorithms]

=== Maths ===
* [https://eduwiki.innopolis.university/index.php/BSc:Logic_and_Discrete_Mathematics.f22 CSE113 — Philosophy I - (Discrete Math and Logic)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I.f22 CSE201 — Mathematical Analysis I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I.f22 CSE202 — Analytical Geometry and Linear Algebra I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II.s23 CSE203 — Mathematical Analysis II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II.s23 CSE204 — Analytic Geometry And Linear Algebra II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Differential_Equations.f22 CSE205 — Differential Equations]
* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics.f22 CSE206 — Probability And Statistics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_to_Optimization.F22 CSE??? — Introduction to Optimization]

=== AI ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Artificial_Intelligence CSE301 — Introduction to Artificial Intelligence]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Machine_Learning CSE302 — Introduction to Machine Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Big_Data CSE303 — Introduction to Big Data]
* [https://eduwiki.innopolis.university/index.php/BSc:_Game_Theory CSE304 — Game Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Mining CSE305 — Data Mining]
* [https://eduwiki.innopolis.university/index.php/BSc:_Information_Retrieval CSE306 — Information Retrieval]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Computer_Vision CSE307 — Introduction to Computer Vision]
* [https://eduwiki.innopolis.university/index.php/BSc:_Practical_Machine_Learning_Deep_Learning CSE308 — Practical Machine Learning And Deep Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Statistical_Techniques_For_Data_Science CSE310 — Statistical Techniques for Data Science and Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Natural_Language_Processing CSE311 — Natural Language Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Nature_Inspired_Computing CSE340 — Nature Inspired Computing]
----

=== Hardware and Robotics ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Computer_Architecture CSE401 — Fundamentals of Computer Architecture]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_I CSE402 — Physics I (Mechanics)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Control_Theory CSE403 — Control Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Sensors_And_Sensing CSE404 — Sensors and Sensing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechatronics CSE405 — Mechatronics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Robotics CSE406 — Fundamentals of Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechanics_And_Machines CSE407 — Mechanics And Machines]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Mechanics CSE408 — Theoretical Mechanics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Robotic_Systems CSE409 — Robotic Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_II CSE410 — Physics II - Electrical Engineering]

----

=== Security and Networks ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Networks CSE501 — Networks]
* [https://eduwiki.innopolis.university/index.php/BSc:_System_And_Network_Administration CSE502 — System And Network Administration]
* [https://eduwiki.innopolis.university/index.php/BSc:_Network_And_Cyber_Security CSE503 — Network And Cyber Security]
* [https://eduwiki.innopolis.university/index.php/BSc:_Signals_And_Systems CSE504 — Digital Signal Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Dev_Ops_Engineering CSE508 — DevOps Engineering]
* [https://eduwiki.innopolis.university/index.php/BSc:_Fundamentals_of_Computer_Security CSE509 — Fundamentals of Information Security]

=== SE ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Lean_Software_Development CSE803 — Lean Software Development]
* [https://eduwiki.innopolis.university/index.php/BSc:SoftwareQualityandReliability.S22 CSE806 — Software Quality and Reliability]
* [https://eduwiki.innopolis.university/index.php/Bc:_It_Product_Development CSE807 — IT Product Development]

== Humanities ==

=== Pedagogy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_History HSS601 — History (History of Russia, World History)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physical_Culture_and_Sport HSS105 — Physical Culture and Sport]

=== Languages and literature ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture HSS203 — Foreign Language I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS204 — Foreign Language II]

=== The science ===
* [https://eduwiki.innopolis.university/index.php/BSc:AcademicResearchandWritingCulture HSS501 — Academic Research and Writing Culture 1]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS502 — Academic Research and Writing Culture 2]

=== History and philosophy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Philosophy_II HSS602 — Philosophy II (Languages and Perceptions)]

----

BSc:Syllabi Index

2022-07-21T13:59:38Z

R.akhmetzyanov: /* Technical */

== Technical ==

=== Computer Science, Programming ===

=== Maths ===
* [https://eduwiki.innopolis.university/index.php/BSc:Logic_and_Discrete_Mathematics.f22 CSE113 — Philosophy I - (Discrete Math and Logic)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I.f22 CSE201 — Mathematical Analysis I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I.f22 CSE202 — Analytical Geometry and Linear Algebra I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II.s23 CSE203 — Mathematical Analysis II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II.s23 CSE204 — Analytic Geometry And Linear Algebra II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Differential_Equations.f22 CSE205 — Differential Equations]
* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics.f22 CSE206 — Probability And Statistics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_to_Optimization.F22 CSE??? — Introduction to Optimization]

=== AI ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Artificial_Intelligence CSE301 — Introduction to Artificial Intelligence]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Machine_Learning CSE302 — Introduction to Machine Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Big_Data CSE303 — Introduction to Big Data]
* [https://eduwiki.innopolis.university/index.php/BSc:_Game_Theory CSE304 — Game Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Mining CSE305 — Data Mining]
* [https://eduwiki.innopolis.university/index.php/BSc:_Information_Retrieval CSE306 — Information Retrieval]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Computer_Vision CSE307 — Introduction to Computer Vision]
* [https://eduwiki.innopolis.university/index.php/BSc:_Practical_Machine_Learning_Deep_Learning CSE308 — Practical Machine Learning And Deep Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Statistical_Techniques_For_Data_Science CSE310 — Statistical Techniques for Data Science and Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Natural_Language_Processing CSE311 — Natural Language Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Nature_Inspired_Computing CSE340 — Nature Inspired Computing]
----

=== Hardware and Robotics ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Computer_Architecture CSE401 — Fundamentals of Computer Architecture]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_I CSE402 — Physics I (Mechanics)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Control_Theory CSE403 — Control Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Sensors_And_Sensing CSE404 — Sensors and Sensing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechatronics CSE405 — Mechatronics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Robotics CSE406 — Fundamentals of Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechanics_And_Machines CSE407 — Mechanics And Machines]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Mechanics CSE408 — Theoretical Mechanics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Robotic_Systems CSE409 — Robotic Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_II CSE410 — Physics II - Electrical Engineering]

----

=== Security and Networks ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Networks CSE501 — Networks]
* [https://eduwiki.innopolis.university/index.php/BSc:_System_And_Network_Administration CSE502 — System And Network Administration]
* [https://eduwiki.innopolis.university/index.php/BSc:_Network_And_Cyber_Security CSE503 — Network And Cyber Security]
* [https://eduwiki.innopolis.university/index.php/BSc:_Signals_And_Systems CSE504 — Digital Signal Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Dev_Ops_Engineering CSE508 — DevOps Engineering]
* [https://eduwiki.innopolis.university/index.php/BSc:_Fundamentals_of_Computer_Security CSE509 — Fundamentals of Information Security]

=== SE ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Lean_Software_Development CSE803 — Lean Software Development]
* [https://eduwiki.innopolis.university/index.php/BSc:SoftwareQualityandReliability.S22 CSE806 — Software Quality and Reliability]
* [https://eduwiki.innopolis.university/index.php/Bc:_It_Product_Development CSE807 — IT Product Development]

== Humanities ==

=== Pedagogy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_History HSS601 — History (History of Russia, World History)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physical_Culture_and_Sport HSS105 — Physical Culture and Sport]

=== Languages and literature ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture HSS203 — Foreign Language I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS204 — Foreign Language II]

=== The science ===
* [https://eduwiki.innopolis.university/index.php/BSc:AcademicResearchandWritingCulture HSS501 — Academic Research and Writing Culture 1]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS502 — Academic Research and Writing Culture 2]

=== History and philosophy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Philosophy_II HSS602 — Philosophy II (Languages and Perceptions)]

----

BSc:Syllabi Index

2022-07-21T13:59:03Z

R.akhmetzyanov: /* Technical */

== Technical ==

=== Computer Science, Programming ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming CSE101 — Introduction to Programming I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming_II CSE102 — Introduction to Programming II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Computer_Science CSE103 — Theoretical Computer Science]
* [https://eduwiki.innopolis.university/index.php/BSc:_Operating_Systems CSE105 — Operating Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Databases CSE106 — Databases]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Compilers_Construction_and_Program_Analysis CSE108 — Advanced Compilers Construction and Program Analysis]
* [https://eduwiki.innopolis.university/index.php/BSc:_Programming_Paradigms CSE109 — Programming Paradigms]
* [https://eduwiki.innopolis.university/index.php/BSc:_Software_Systems_Design CSE112 — Software Systems Analysis and Design]
* [https://eduwiki.innopolis.university/index.php/BSc:_Distributed_And_Network_Programming CSE114 — Distributed And Network Programming]
* [https://eduwiki.innopolis.university/index.php/BSc:_Compilers_Construction CSE115 — Compilers Construction]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Structures_Algorithms CSE117 — Data Structures and Algorithms]

=== Maths ===
* [https://eduwiki.innopolis.university/index.php/BSc:Logic_and_Discrete_Mathematics.f22 CSE113 — Philosophy I - (Discrete Math and Logic)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I.f22 CSE201 — Mathematical Analysis I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I.f22 CSE202 — Analytical Geometry and Linear Algebra I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II.s23 CSE203 — Mathematical Analysis II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II.s23 CSE204 — Analytic Geometry And Linear Algebra II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Differential_Equations.f22 CSE205 — Differential Equations]
* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics.f22 CSE206 — Probability And Statistics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_to_Optimization.F22 CSE??? — Introduction to Optimization]

=== AI ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Artificial_Intelligence CSE301 — Introduction to Artificial Intelligence]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Machine_Learning CSE302 — Introduction to Machine Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Big_Data CSE303 — Introduction to Big Data]
* [https://eduwiki.innopolis.university/index.php/BSc:_Game_Theory CSE304 — Game Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Mining CSE305 — Data Mining]
* [https://eduwiki.innopolis.university/index.php/BSc:_Information_Retrieval CSE306 — Information Retrieval]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Computer_Vision CSE307 — Introduction to Computer Vision]
* [https://eduwiki.innopolis.university/index.php/BSc:_Practical_Machine_Learning_Deep_Learning CSE308 — Practical Machine Learning And Deep Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Statistical_Techniques_For_Data_Science CSE310 — Statistical Techniques for Data Science and Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Natural_Language_Processing CSE311 — Natural Language Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Nature_Inspired_Computing CSE340 — Nature Inspired Computing]
----

=== Hardware and Robotics ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Computer_Architecture CSE401 — Fundamentals of Computer Architecture]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_I CSE402 — Physics I (Mechanics)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Control_Theory CSE403 — Control Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Sensors_And_Sensing CSE404 — Sensors and Sensing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechatronics CSE405 — Mechatronics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Robotics CSE406 — Fundamentals of Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechanics_And_Machines CSE407 — Mechanics And Machines]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Mechanics CSE408 — Theoretical Mechanics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Robotic_Systems CSE409 — Robotic Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_II CSE410 — Physics II - Electrical Engineering]

----

=== Security and Networks ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Networks CSE501 — Networks]
* [https://eduwiki.innopolis.university/index.php/BSc:_System_And_Network_Administration CSE502 — System And Network Administration]
* [https://eduwiki.innopolis.university/index.php/BSc:_Network_And_Cyber_Security CSE503 — Network And Cyber Security]
* [https://eduwiki.innopolis.university/index.php/BSc:_Signals_And_Systems CSE504 — Digital Signal Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Dev_Ops_Engineering CSE508 — DevOps Engineering]
* [https://eduwiki.innopolis.university/index.php/BSc:_Fundamentals_of_Computer_Security CSE509 — Fundamentals of Information Security]

=== SE ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Lean_Software_Development CSE803 — Lean Software Development]
* [https://eduwiki.innopolis.university/index.php/BSc:SoftwareQualityandReliability.S22 CSE806 — Software Quality and Reliability]
* [https://eduwiki.innopolis.university/index.php/Bc:_It_Product_Development CSE807 — IT Product Development]

== Humanities ==

=== Pedagogy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_History HSS601 — History (History of Russia, World History)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physical_Culture_and_Sport HSS105 — Physical Culture and Sport]

=== Languages and literature ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture HSS203 — Foreign Language I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS204 — Foreign Language II]

=== The science ===
* [https://eduwiki.innopolis.university/index.php/BSc:AcademicResearchandWritingCulture HSS501 — Academic Research and Writing Culture 1]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS502 — Academic Research and Writing Culture 2]

=== History and philosophy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Philosophy_II HSS602 — Philosophy II (Languages and Perceptions)]

----

BSc:Syllabi Index

2022-07-21T13:58:28Z

R.akhmetzyanov: /* Technical */

== Technical ==

=== Computer Science, Programming ===
/* to do */
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming CSE101 — Introduction to Programming I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming_II CSE102 — Introduction to Programming II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Computer_Science CSE103 — Theoretical Computer Science]
* [https://eduwiki.innopolis.university/index.php/BSc:_Operating_Systems CSE105 — Operating Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Databases CSE106 — Databases]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Compilers_Construction_and_Program_Analysis CSE108 — Advanced Compilers Construction and Program Analysis]
* [https://eduwiki.innopolis.university/index.php/BSc:_Programming_Paradigms CSE109 — Programming Paradigms]
* [https://eduwiki.innopolis.university/index.php/BSc:_Software_Systems_Design CSE112 — Software Systems Analysis and Design]
* [https://eduwiki.innopolis.university/index.php/BSc:_Distributed_And_Network_Programming CSE114 — Distributed And Network Programming]
* [https://eduwiki.innopolis.university/index.php/BSc:_Compilers_Construction CSE115 — Compilers Construction]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Structures_Algorithms CSE117 — Data Structures and Algorithms]

=== Maths ===
* [https://eduwiki.innopolis.university/index.php/BSc:Logic_and_Discrete_Mathematics.f22 CSE113 — Philosophy I - (Discrete Math and Logic)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I.f22 CSE201 — Mathematical Analysis I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I.f22 CSE202 — Analytical Geometry and Linear Algebra I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II.s23 CSE203 — Mathematical Analysis II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II.s23 CSE204 — Analytic Geometry And Linear Algebra II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Differential_Equations.f22 CSE205 — Differential Equations]
* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics.f22 CSE206 — Probability And Statistics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_to_Optimization.F22 CSE??? — Introduction to Optimization]

=== AI ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Artificial_Intelligence CSE301 — Introduction to Artificial Intelligence]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Machine_Learning CSE302 — Introduction to Machine Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Big_Data CSE303 — Introduction to Big Data]
* [https://eduwiki.innopolis.university/index.php/BSc:_Game_Theory CSE304 — Game Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Mining CSE305 — Data Mining]
* [https://eduwiki.innopolis.university/index.php/BSc:_Information_Retrieval CSE306 — Information Retrieval]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Computer_Vision CSE307 — Introduction to Computer Vision]
* [https://eduwiki.innopolis.university/index.php/BSc:_Practical_Machine_Learning_Deep_Learning CSE308 — Practical Machine Learning And Deep Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Statistical_Techniques_For_Data_Science CSE310 — Statistical Techniques for Data Science and Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Natural_Language_Processing CSE311 — Natural Language Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Nature_Inspired_Computing CSE340 — Nature Inspired Computing]
----

=== Hardware and Robotics ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Computer_Architecture CSE401 — Fundamentals of Computer Architecture]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_I CSE402 — Physics I (Mechanics)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Control_Theory CSE403 — Control Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Sensors_And_Sensing CSE404 — Sensors and Sensing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechatronics CSE405 — Mechatronics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Robotics CSE406 — Fundamentals of Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechanics_And_Machines CSE407 — Mechanics And Machines]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Mechanics CSE408 — Theoretical Mechanics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Robotic_Systems CSE409 — Robotic Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_II CSE410 — Physics II - Electrical Engineering]

----

=== Security and Networks ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Networks CSE501 — Networks]
* [https://eduwiki.innopolis.university/index.php/BSc:_System_And_Network_Administration CSE502 — System And Network Administration]
* [https://eduwiki.innopolis.university/index.php/BSc:_Network_And_Cyber_Security CSE503 — Network And Cyber Security]
* [https://eduwiki.innopolis.university/index.php/BSc:_Signals_And_Systems CSE504 — Digital Signal Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Dev_Ops_Engineering CSE508 — DevOps Engineering]
* [https://eduwiki.innopolis.university/index.php/BSc:_Fundamentals_of_Computer_Security CSE509 — Fundamentals of Information Security]

=== SE ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Lean_Software_Development CSE803 — Lean Software Development]
* [https://eduwiki.innopolis.university/index.php/BSc:SoftwareQualityandReliability.S22 CSE806 — Software Quality and Reliability]
* [https://eduwiki.innopolis.university/index.php/Bc:_It_Product_Development CSE807 — IT Product Development]

== Humanities ==

=== Pedagogy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_History HSS601 — History (History of Russia, World History)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physical_Culture_and_Sport HSS105 — Physical Culture and Sport]

=== Languages and literature ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture HSS203 — Foreign Language I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS204 — Foreign Language II]

=== The science ===
* [https://eduwiki.innopolis.university/index.php/BSc:AcademicResearchandWritingCulture HSS501 — Academic Research and Writing Culture 1]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS502 — Academic Research and Writing Culture 2]

=== History and philosophy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Philosophy_II HSS602 — Philosophy II (Languages and Perceptions)]

----

BSc:Syllabi Index

2022-07-21T13:58:09Z

R.akhmetzyanov: /* Technical */

== Technical ==

=== Computer Science, Programming ===
# to do
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming CSE101 — Introduction to Programming I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming_II CSE102 — Introduction to Programming II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Computer_Science CSE103 — Theoretical Computer Science]
* [https://eduwiki.innopolis.university/index.php/BSc:_Operating_Systems CSE105 — Operating Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Databases CSE106 — Databases]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Compilers_Construction_and_Program_Analysis CSE108 — Advanced Compilers Construction and Program Analysis]
* [https://eduwiki.innopolis.university/index.php/BSc:_Programming_Paradigms CSE109 — Programming Paradigms]
* [https://eduwiki.innopolis.university/index.php/BSc:_Software_Systems_Design CSE112 — Software Systems Analysis and Design]
* [https://eduwiki.innopolis.university/index.php/BSc:_Distributed_And_Network_Programming CSE114 — Distributed And Network Programming]
* [https://eduwiki.innopolis.university/index.php/BSc:_Compilers_Construction CSE115 — Compilers Construction]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Structures_Algorithms CSE117 — Data Structures and Algorithms]

=== Maths ===
* [https://eduwiki.innopolis.university/index.php/BSc:Logic_and_Discrete_Mathematics.f22 CSE113 — Philosophy I - (Discrete Math and Logic)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I.f22 CSE201 — Mathematical Analysis I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I.f22 CSE202 — Analytical Geometry and Linear Algebra I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II.s23 CSE203 — Mathematical Analysis II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II.s23 CSE204 — Analytic Geometry And Linear Algebra II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Differential_Equations.f22 CSE205 — Differential Equations]
* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics.f22 CSE206 — Probability And Statistics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_to_Optimization.F22 CSE??? — Introduction to Optimization]

=== AI ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Artificial_Intelligence CSE301 — Introduction to Artificial Intelligence]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Machine_Learning CSE302 — Introduction to Machine Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Big_Data CSE303 — Introduction to Big Data]
* [https://eduwiki.innopolis.university/index.php/BSc:_Game_Theory CSE304 — Game Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Mining CSE305 — Data Mining]
* [https://eduwiki.innopolis.university/index.php/BSc:_Information_Retrieval CSE306 — Information Retrieval]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Computer_Vision CSE307 — Introduction to Computer Vision]
* [https://eduwiki.innopolis.university/index.php/BSc:_Practical_Machine_Learning_Deep_Learning CSE308 — Practical Machine Learning And Deep Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Statistical_Techniques_For_Data_Science CSE310 — Statistical Techniques for Data Science and Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Natural_Language_Processing CSE311 — Natural Language Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Nature_Inspired_Computing CSE340 — Nature Inspired Computing]
----

=== Hardware and Robotics ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Computer_Architecture CSE401 — Fundamentals of Computer Architecture]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_I CSE402 — Physics I (Mechanics)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Control_Theory CSE403 — Control Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Sensors_And_Sensing CSE404 — Sensors and Sensing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechatronics CSE405 — Mechatronics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Robotics CSE406 — Fundamentals of Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechanics_And_Machines CSE407 — Mechanics And Machines]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Mechanics CSE408 — Theoretical Mechanics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Robotic_Systems CSE409 — Robotic Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_II CSE410 — Physics II - Electrical Engineering]

----

=== Security and Networks ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Networks CSE501 — Networks]
* [https://eduwiki.innopolis.university/index.php/BSc:_System_And_Network_Administration CSE502 — System And Network Administration]
* [https://eduwiki.innopolis.university/index.php/BSc:_Network_And_Cyber_Security CSE503 — Network And Cyber Security]
* [https://eduwiki.innopolis.university/index.php/BSc:_Signals_And_Systems CSE504 — Digital Signal Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Dev_Ops_Engineering CSE508 — DevOps Engineering]
* [https://eduwiki.innopolis.university/index.php/BSc:_Fundamentals_of_Computer_Security CSE509 — Fundamentals of Information Security]

=== SE ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Lean_Software_Development CSE803 — Lean Software Development]
* [https://eduwiki.innopolis.university/index.php/BSc:SoftwareQualityandReliability.S22 CSE806 — Software Quality and Reliability]
* [https://eduwiki.innopolis.university/index.php/Bc:_It_Product_Development CSE807 — IT Product Development]

== Humanities ==

=== Pedagogy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_History HSS601 — History (History of Russia, World History)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physical_Culture_and_Sport HSS105 — Physical Culture and Sport]

=== Languages and literature ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture HSS203 — Foreign Language I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS204 — Foreign Language II]

=== The science ===
* [https://eduwiki.innopolis.university/index.php/BSc:AcademicResearchandWritingCulture HSS501 — Academic Research and Writing Culture 1]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS502 — Academic Research and Writing Culture 2]

=== History and philosophy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Philosophy_II HSS602 — Philosophy II (Languages and Perceptions)]

----

MSTE: Leadership in entrepreneurship

2022-07-19T18:41:41Z

R.akhmetzyanov: /* The retake exam */

= Leadership_In_Entrepreneurship =
* '''Course name''': Leadership_In_Entrepreneurship
* '''Code discipline''':
* '''Subject area''': Technological Entrepreneurship

== Short Description ==
This course proposes to the students the new vision of the entrepreneur as a leader. Modern management system crucially changed from the role of entrepreneur as a formal “boss” to the role of a creator, innovator and moreover, a leader of the team, leader of community, and leader of positive changes who creates added value. During this course we will create a personal mission and develop the personal brand of an entrepreneur as a leader. We will discuss how modern entrepreneurs and their teams change whole industries and make an important impact on society. The students will develop their leadership skills in a very practical way - leading their startups with added value for their team, business products, clients and moreover the community/society.

== Prerequisites ==

=== Prerequisite subjects ===
* N/A

=== Prerequisite topics ===
* Entrepreneur as a leader creating added values and impact
* Personal brand of a leader
* Entrepreneur as a leader of his life, a leader of his team, a leader of positive changes

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Modern approach to the Leadership in Entrepreneurship ||
# Leadership in entrepreneurship: modern trends
# Famous entrepreneurs as leaders: case study
# Key concepts of leadership in management
# Key leadership skills
# Leadership in IT-industry
|-
| Personal brand of a leader ||
# Creation of personal brand
# Networking of a leader
# Energy as a main resource of a leader
# Personal mission of an entrepreneur
# Leadership in team building and team-management
|-
| Entrepreneur as a Leader of positive changes ||
# Entrepreneurs who changed the world for better: case study
# Leadership during “turbulent” times
# Community leadership
# Emotional and Dialogical leadership
# Entrepreneur as a leader of positive changes
|}
== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to enable students to form the leader identity in entrepreneurship, to actualize their leadership potential and to develop leadership skills.

=== ILOs defined at three levels ===

==== Level 1: What concepts should a student know/remember/explain? ====
By the end of the course, the students should be able to ...
* Describe
* what is leadership in entrepreneurship and name mane modern trends of entrepreneurship in IT-industry,
* how famous entrepreneurs influenced the IT-industry and made an impact,
* key concepts of leadership in management,
* key leadership skills,
* what is a personal brand of IT-entrepreneur,
* basic rules of networking of a leader,
* why energy became a main resource of a leader,
* what is personal mission of an entrepreneur,
* how is leadership implied in team building and team-management,
* feature of leadership during “turbulent” times,
* what is community leadership,
* what is emotional and dialogical leadership,
* how an entrepreneur can become a leader of positive changes.

==== Level 2: What basic practical skills should a student be able to perform? ====
By the end of the course, the students should be able to ...
* Formulate
* leadership skills in entrepreneurship
* proactivity, motivation, goal-orientation,
* personal branding,
* networking,
* skills assisting to raise the energetic level of an entrepreneur
* team building and team-management
* flexibility, ability to manage in “turbulent” times,
* community leadership
* emotional and dialogical leadership

==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
By the end of the course, the students should be able to ...
* Conduct
* leadership in IT-entrepreneurship,
* creation of personal brand,
* networking,
* creation of personal mission,
* team building and team-management
* leadership during “turbulent” times
* community leadership
* emotional and dialogical leadership
* leadership of positive changes.
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 90-100 || -
|-
| B. Good || 75-89 || -
|-
| C. Satisfactory || 60-74 || -
|-
| D. Fail || 0-59 || -
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Assignment || 50
|-
| Quizzes || 15
|-
| Peer review || 15
|-
| Demo day || 20
|}

=== Recommendations for students on how to succeed in the course ===
Participation is important. Showing up is the key to success in this course. You will work in teams, so coordinating teamwork will be an important factor for success. This is also reflected in the peer review being a graded item. Review lecture materials before classes to do well in practical tasks and competencies development. Reading the recommended literature is optional, and will give you a deeper understanding of the material.

== Resources, literature and reference materials ==

=== Open access resources ===
* Essential Leadership Skills Every Entrepreneur Should Continually Hone https://www.forbes.com/sites/theyec/2021/11/04/10-essential-leadership-skills-every-entrepreneur-should-continually-hone/?sh=2ac99c94fe94
* World Class Leadership Styles That All Entrepreneurs Must Know
* Personal Branding Guidelines for Entrepreneurs https://www.forbes.com/sites/theyec/2020/02/12/personal-branding-guidelines-for-entrepreneurs/?sh=16a033d17ff4

=== Closed access resources ===
* Toward Entrepreneurial Community Development (Routledge Studies in Entrepreneurship) 1st Edition (2019). Ed. Michael Fortunado. Routledge.
* Branson, R. (2006) Screw it, let’s do it. Virgin books.
* Ye, L. (2019) Personal Branding for Entrepreneurs: Proven Personal Branding Strategy and Why Social Media Marketing is Crucial for Your Business

=== Software and tools used within the course ===
* N\A
= Teaching Methodology: Methods, techniques, & activities =

== Activities and Teaching Methods ==
{| class="wikitable"
|+ Teaching and Learning Methods within each section
|-
! Teaching Techniques !! Section 1 !! Section 2 !! Section 3
|-
| Problem-based learning (students learn by solving open-ended problems without a strictly-defined solution) || 1 || 1 || 1
|-
| Project-based learning (students work on a project) || 1 || 1 || 1
|-
| Modular learning (facilitated self-study) || 1 || 1 || 1
|-
| Differentiated learning (provide tasks and activities at several levels of difficulty to fit students needs and level) || 1 || 1 || 1
|-
| Contextual learning (activities and tasks are connected to the real world to make it easier for students to relate to them); || 1 || 1 || 1
|-
| Business game (learn by playing a game that incorporates the principles of the material covered within the course). || 1 || 1 || 1
|-
| развивающего обучения (задания и материал "прокачивают" ещё нераскрытые возможности студентов); || 1 || 1 || 1
|-
| концентрированного обучения (занятия по одной большой теме логически объединяются); || 1 || 1 || 1
|-
| inquiry-based learning || 1 || 1 || 1
|-
| Just-in-time teaching || 1 || 1 || 1
|-
| Process oriented guided inquiry learning (POGIL) || 1 || 1 || 1
|-
| Studio-based learning || 1 || 1 || 1
|-
| Universal design for learning, || 1 || 1 || 1
|-
| Task-based learning || 1 || 1 || 1
|}
{| class="wikitable"
|+ Activities within each section
|-
! Learning Activities !! Section 1 !! Section 2 !! Section 3
|-
| Lectures || 1 || 1 || 1
|-
| Interactive Lectures || 1 || 1 || 1
|-
| Cases studies || 1 || 1 || 1
|-
| Group projects || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1
|-
| Presentations by students || 1 || 1 || 1
|-
| Written reports || 1 || 1 || 1
|-
| Simulations and role-plays || 1 || 1 || 1
|-
| Essays || 1 || 1 || 1
|-
| Oral Reports || 1 || 1 || 1
|-
| Individual Projects || 0 || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Group discussion || IT-Entrepreneur as a leader || 1
|-
| Case study || Famous entrepreneurs as leaders || 1
|-
| Group Project Work || Key leadership skills in IT-entrepreneurship || 1
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Individual project || Personal mission of entrepreneur || 1
|-
| Business training || Energy as a main resource of entrepreneur || 0
|-
| Business game || Team Building in entrepreneurship || 1
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Case study || Entrepreneurs who changed the world for better || 1
|-
| Business training || Emotional and Dialogical Leadership || 0
|-
| Group project work || IT-entrepreneur as a leader of positive changes || 1
|}
=== Final assessment ===
'''Section 1'''
# Grading criteria for the final project presentation:
# Student shortly describes and gives his opinion on main trends for leaders in IT-sector
# Student lists and explains his main leadership skills in entrepreneurship
'''Section 2'''
# Student presents his personal brand concept and his personal mission as an entrepreneur
# Student presents his network and provides the strategy of its development
# Student presents his team and his team building strategy.
'''Section 3'''
# Using all previous knowledge and skills a student makes a presentation “Me as a leader of positive changes” where he explains how his business project in IT-sphere can create a positive impact.

=== The retake exam ===

For the retake, students have to improve their project according to the recommendation provided by the professor during the final exam.

MSc: Founder project

2022-07-19T18:35:49Z

R.akhmetzyanov: /* The retake exam */

= Founder_Project =
* '''Course name''': Founder_Project
* '''Code discipline''':
* '''Subject area''': Technical Entrepreneurship

== Short Description ==
This course is a combination of mentorship and workshops created for the support and development of a student-founder during his study. All workshops are practically oriented and help to systematize and practically imply all knowledge and skills. Moreover, the workshop has an important psychological meaning for a founder - they are designed to help to reflect, get the answers for important questions and overcome the challenges. Individual work with mentors is combined with group meetings where students progress the creation of their community of the founders and can share their opinions, thoughts, etc. In addition to that, the workshops are targeting to train founders to effectively face important events/cases (“critical incidents”) that happen in their entrepreneurial life.

== Prerequisites ==

=== Prerequisite subjects ===
* N/A

=== Prerequisite topics ===
* Progress check-up, Q&A session, Critical incidents (CI) analysis, Reflection and feedback
* Management of a startup: key issues
* Soft and hard skills of a founder

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Section 1: Basic ||
# Basic for all topics:
# Progress check-up
# Q&A session
# Critical incidents (CI) analysis
# Reflection and feedback
# Main topics for the workshops:
# Being an entrepreneur: challenges and advantages
# How to work with a mentor
# Science and entrepreneurship: synergy effect
# Step by step to success: how to measure your business progress
# Developing entrepreneurial thinking
# Self presentation and pitching
# Techniques of self-effectiveness for an entrepreneur
# Business mission: creating value on the market
# Planning and strategic projecting
# Teambuilding
# Problem-solving and flexibility in entrepreneurship
# Customer relationships
# Rise and fall”: how to learn from your mistakes
# Work/life balance for an entrepreneur
# Where to find inspiration and to inspire others in business
|-
| Section 2: Advanced ||
# Basic for all topics:
# Progress check-up
# Q&A session
# Critical incidents (CI) analysis
# Reflection and feedback
# Main topics for the workshops:
# Forming entrepreneurial identity: how to face CI effectively
# Entrepreneurial strategies
# Competitiveness and high professional value of a startup founder
# Mentorship and advisory for a startup
# Investor relationship
# Community management of IT-entrepreneur
# Implementation of a business model: flexibility and adaptation
# Team building and human potential development
# Networking: how to add and create value
# Finance and money attitude of a startup founder
# Client orientation
# Pitch-deck design and pitching to various audiences
# Startup founder as a seller
# Risk-management for an IT-startup
# Creating brand that matters: how to make a valuable impact
|}
== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to enable a student to progress with the development of the business project effectively dealing with a mentor and participating in workshops on main topics needed for a startup founder.

=== ILOs defined at three levels ===

==== Level 1: What concepts should a student know/remember/explain? ====
By the end of the course, the students should be able to ...
* know what is a progress check up, Critical incident, reflection in entrepreneurship,
* explain main management concepts for a startup
* explain all basic elements needed for a project realization
* explain challenges and advantages of being a startup founder
* know what is entrepreneurial thinking and how to develop it,
* basic concepts of working with mentor

==== Level 2: What basic practical skills should a student be able to perform? ====
By the end of the course, the students should be able to ...
* deal with a mentor and advisors,
* use scientific research in business,
* measure business progress,
* do self presentation and pitching,
* create a business mission,
* plan and do strategic projecting
* team building,
* perform problem-solving and flexibility,
* manage customer relationships,
* get right conclusions from the mistakes,
* organize work/life balance,
* to inspire others in business,
* face CI effectively using various entrepreneurial strategies,
* provide startup and self-competitiveness,
* manage with investors,
* do networking and community management
* pitch to different audiences,
* implement client orientation to their company,
* risk-management,
* branding.

==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
By the end of the course, the students should be able to ...
* Check the progress of a startup, make a reflection, face with the main critical incidents for a founder
* Effectively deal with a mentor, participate in the student business community providing a value,
* Develop a founder project systematizing all concepts and using all basic elements of startup management.
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 90-100 || -
|-
| B. Good || 75-89 || -
|-
| C. Satisfactory || 60-74 || -
|-
| D. Fail || 0-59 || -
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Assignment || 50
|-
| Quizzes || 15
|-
| Peer review || 15
|-
| Demo day || 20
|}

=== Recommendations for students on how to succeed in the course ===
Participation is important. Showing up is the key to success in this course. You will work in teams, so coordinating teamwork will be an important factor for success. This is also reflected in the peer review being a graded item. Review lecture materials before classes to do well in quizzes. Reading the recommended literature is optional, and will give you a deeper understanding of the material.

== Resources, literature and reference materials ==

=== Open access resources ===
* Five insightful TED talks every startup founder should watch
* How to find a mentor
* How to succeed with a statup
* How to build a future https://www.ycombinator.com/library/6G-how-to-build-the-future

=== Closed access resources ===
* The 23 Best Books for Startup Founders at Any Stage https://visible.vc/blog/books-for-founders/

=== Software and tools used within the course ===

= Teaching Methodology: Methods, techniques, & activities =

== Activities and Teaching Methods ==
{| class="wikitable"
|+ Teaching and Learning Methods within each section
|-
! Teaching Techniques !! Section 1 !! Section 2
|-
| Problem-based learning (students learn by solving open-ended problems without a strictly-defined solution) || 1 || 1
|-
| Project-based learning (students work on a project) || 1 || 1
|-
| Contextual learning (activities and tasks are connected to the real world to make it easier for students to relate to them); || 1 || 1
|-
| Business game (learn by playing a game that incorporates the principles of the material covered within the course). || 1 || 1
|-
| развивающего обучения (задания и материал "прокачивают" ещё нераскрытые возможности студентов); || 1 || 1
|-
| концентрированного обучения (занятия по одной большой теме логически объединяются); || 1 || 1
|-
| inquiry-based learning || 1 || 1
|-
| Just-in-time teaching || 1 || 1
|}
{| class="wikitable"
|+ Activities within each section
|-
! Learning Activities !! Section 1 !! Section 2
|-
| Lab exercises || 1 || 1
|-
| Cases studies || 1 || 1
|-
| Individual Projects || 1 || 1
|-
| Group projects || 1 || 1
|-
| Discussions || 1 || 1
|-
| Presentations by students || 1 || 1
|-
| Simulations and role-plays || 1 || 1
|-
| Oral Reports || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Individual and group reflection || Progress check-up Q&A session Critical incidents (CI) analysis Reflection and feedback || 1
|-
| Training || Business training on the topic of the workshop with the aim to concept understanding, the skills development, knowledge systematization and practical implementation. || 0
|-
| Group project || 5-minutes group pitches on the topic of the current class. Suggested structure: - What is the main problem a Founder can face here? - What are the main techniques/instruments he can use? - What are the main strategies of implementation? - What is a practical value? - What else can we propose? || 1
|-
| Individual presentation || Students make presentations showing the ways of practical implementation of the business aspect that was discussed in current class into their startup. || 1
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Individual and group reflection || Progress check-up Q&A session Critical incidents (CI) analysis Reflection and feedback || 1
|-
| Training || Business training on the topic of the workshop with the aim to concept understanding, the skills development, knowledge systematization and practical implementation. || 0
|-
| Group project || 5-minutes group pitches on the topic of the current class. Suggested structure: - What is the main problem a Founder can face here? - What are the main techniques/instruments he can use? - What are the main strategies of implementation? - What is a practical value? - What else can we propose? || 1
|-
| Individual presentation || Students make presentations showing the ways of practical implementation of the business aspect that was discussed in current class into their startup. || 1
|}
=== Final assessment ===
'''Section 1'''
# Grading criteria for the final project presentation:
# The student shows his ability to measure business progress, reflect and make conclusions,
# The student can show that he can face main business critical incidents effectively using various strategies,
# Students show that his dealing with a mentor is productive,
# Demonstrates effective practical implementation of the following concepts/issues: implementation of scientific work into business practice, business mission, strategical projecting, team building, problem-solving and flexibility, customer relationship, work/life balance.
# Performs good communication, pitching and presentation skills, inspired by the project, eager to develop it.
'''Section 2'''
# Grading criteria for the final project presentation:
# The student shows his ability to measure business progress, reflect and make conclusions,
# The student can show that he can face main business critical incidents effectively using various strategies,
# Students show that his dealing with a mentor is productive,
# Demonstrates effective practical implementation of the following concepts/issues: founder’s competitiveness and professional value, investor relationship, community management, business modeling, finance management, networking, team building and human potential management, client orientations, sales, risk-management and branding.
# Performs good communication, pitching and presentation skills, inspired by the project, eager to develop it.

=== The retake exam ===
For the retake, students have to upgrade their final presentation according to the recommendations provided by the professor during their exam.

MSc: Marketing and sales for it business

2022-07-19T15:54:31Z

R.akhmetzyanov: /* The retake exam */

= Marketing_and_Sales_for_IT_Business =
* '''Course name''': Marketing_and_Sales_for_IT_Business
* '''Code discipline''':
* '''Subject area''': all around marketing and sales in IT industry.

== Short Description ==
This course contains two important for successful company parts: marketing and sales.
These are the parts that are linked with each other - it is very difficult to sell without marketing support and it is very difficult to achieve results with marketing efforts only.
Marketing part, starting from defining things like developing marketing strategy for the companies, finally offers practical tools of digital marketing. We will explore new digital reality and its impact on IT business. We will learn success stories of real businesses and how companies are adapting to the new changing landscape.
The second part of the course covers important things for every company's success – the sales process. Understand how to attract customers in negotiations, how to “get to yes” getting great deals, how to control the sales funnel – you will get the understanding how it works and try it in practice.

== Prerequisites ==

=== Prerequisite subjects ===
* HSS310

=== Prerequisite topics ===
* Basic IT industry knowledge
* Basic marketing knowledge

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Marketing Strategy ||
# Types of markets
# Product-centric marketing
# Customer-centric marketing
# Developing Marketing Strategy
|-
| Marketing tools ||
# Brand&Presentation
# Analytics
# Content
# SMM
# Context advertising
# E-mail marketing
|-
| Sales ||
# CRM systems
# B2B
# B2C
# Negotiations
|-
| Final Project Presentation ||
# Presentation of marketing&sales strategy and tactics for startup
|}
== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
This course aims to give students the skills of developing a winning marketing strategy for a startup, as well as the skills to implement marketing strategy using real digital-marketing tools and sales tactics for a startup product.

=== ILOs defined at three levels ===

==== Level 1: What concepts should a student know/remember/explain? ====
By the end of the course, the students should be able to ...
* Develop naming, presentation, and product offer
* Use digital marketing tools
* Use CRM
* Sell its product

==== Level 2: What basic practical skills should a student be able to perform? ====
By the end of the course, the students should be able to ...
* Skills of market type identification
* Skills in developing naming, presentations, product offerings
* Skills of context advertising
* Skills of SMM doing
* Skills of content marketing
* Skills of e-mail marketing

==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
By the end of the course, the students should be able to ...
* Skills for valuation the market environment
* Skills how to find the right addressable market for its product
* Skills of web analytics
* Skills of CRM using
* Sales skills to various types of clients
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| Pass || 75-100 || -
|-
| Fail || 0-74 || -
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Seminar classes || 40
|-
| Interim performance assessment on the results of lecture assignments and its presentations || 30
|-
| Final presentation || 30
|}

=== Recommendations for students on how to succeed in the course ===
The student is recommended the following scheme of preparation for classes: Marketing and sales are much more about hypothesis testing and math, than creativity. Therefore, it is so important for students to try the acquired knowledge in real practice, doing small tasks after each lecture. Finally, we will try to assemble a working strategy for a startup from these tasks. Moreover: Participation is important. Showing up is the key to success in this course. Reading the recommended literature is optional, and will give you a deeper understanding of the material.

== Resources, literature and reference materials ==

=== Open access resources ===
* Андрей Кравченко. Неидеальная стратегия для идеальной компании.
* Peter Fader. Customer Centricity.

=== Closed access resources ===
* Viktor Pelevin. Empire V.
* W. Chan Kim, Renee Mauborgne. Blue Ocean Strategy.
* Eric ries. Lean startup.
* Simon Kingsnorth. Digital Marketing Strategy.
* Chet Holmes. The Ultimate Sales Machine.

=== Software and tools used within the course ===
* Standard office tools for Tables, Text and Presentation
= Teaching Methodology: Methods, techniques, & activities =

== Activities and Teaching Methods ==
{| class="wikitable"
|+ Teaching and Learning Methods within each section
|-
! Teaching Techniques !! Section 1 !! Section 2 !! Section 3 !! Section 4
|-
| Problem-based learning (students learn by solving open-ended problems without a strictly-defined solution) || 1 || 1 || 1 || 1
|-
| Project-based learning (students work on a project) || 1 || 1 || 1 || 1
|-
| Business game (learn by playing a game that incorporates the principles of the material covered within the course). || 1 || 1 || 1 || 1
|-
| Task-based learning || 1 || 1 || 1 || 1
|}
{| class="wikitable"
|+ Activities within each section
|-
! Learning Activities !! Section 1 !! Section 2 !! Section 3 !! Section 4
|-
| Lectures || 1 || 1 || 1 || 0
|-
| Interactive Lectures || 1 || 1 || 1 || 0
|-
| Lab exercises || 1 || 1 || 1 || 0
|-
| Cases studies || 1 || 1 || 1 || 0
|-
| Individual Projects || 1 || 1 || 1 || 1
|-
| Peer Review || 1 || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1 || 1
|-
| Presentations by students || 1 || 1 || 1 || 1
|-
| Written reports || 1 || 1 || 1 || 1
|-
| Simulations and role-plays || 1 || 1 || 1 || 1
|-
| Experiments || 0 || 1 || 1 || 0
|-
| Group projects || 0 || 0 || 0 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| after lecture assignments || Define target audience and describe type of market for your product. || 1
|-
| after lecture assignments || Make 3 cusdev with potential/existing customers of your product. || 1
|-
| after lecture assignments || Develop your marketing strategy and present it in-class. || 1
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| after lecture assignments || Write a marketing article about your product or technology in the informational style manner. || 1
|-
| after lecture assignments || Create a landing page for your product and connect it to Yandex Metrica or Google Analytics. || 1
|-
| after lecture assignments || Create a semantic core for your product and determine the current positions on your landing page. Determine key marketing metrics, including conversion rate, on your landing page. || 1
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| after lecture assignments || Create the sales funnel of your product and present it in-class. || 1
|-
| after lecture assignments || Create the budget for your marketing and sales activities and approve it with management. || 1
|-
| in-class exercise || “Sell me the pen” exercise. || 1
|}
==== Section 4 ====

=== Final assessment ===
'''Section 1'''
#

For the final assessment, students have to prepare a full project of marketing and sales promotion of their IT product and present it on the exam. The project should contain the next parts:

The idea of your product/service.
Define your market.
Analise what type of market.
Target segment, who should we talk to?
What is your main message(s)?
What should we do to achieve the addressable market?
Brand promotion, knowledge, interest, coverage, sales etc.
Media design.
How should we say it? Creative strategy&content.
Channel (media) strategy.
How do we reach them? Evidence on a real case.
Budget.
Money for promotion.
How to close deals. Evidence on a real case.
Measurement.
How we control the result. Evidence on a real case.

'''Section 2'''

'''Section 3'''

'''Section 4'''

=== The retake exam ===
# The retake exam.

For the retake, students have to implement a product and follow the guidelines of the course. There has to be a meeting before the retake itself to plan and agree on the product ideas, and to answer questions.

MSc: It business start

2022-07-19T15:51:53Z

R.akhmetzyanov: /* IT_Business_Start */

= IT_Business_Start =
* '''Course name''': IT Business Start
* '''Code discipline''':
* '''Subject area''': Technological entrepreneurship

== Short Description ==
This course is for the first-time entrepreneur. We will briefly but concisely discuss all the issues related to starting your own project from scratch: how to make sure that your idea is in demand, how to do market research, how to stop putting off the launch, why the customer is more important than the product, and how to do customer research. During this course, students will get used to their entrepreneurial role, build teams, formulate a business and product idea and be ready to delve into the complexities of business development in the following courses.

== Prerequisites ==

=== Prerequisite subjects ===
* N/A

=== Prerequisite topics ===
* N/A

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Introduction & Building Your Team & Making Your Team Agile ||
# Defining a startup
# Formulating the group project: team, business idea
# Leadership
# Forming the team
# Managing the team
|-
| Defining Your Customer & Defining Your Product & Defining Your Rivals ||
# Customer Segmentation
# Customer Profile (JTBD, Pains, Gains)
# Creating a Value Proposition
# Matching Value Proposition with Customer Profile
# Strategy Canvas
|-
| Defining Your Business Model & Defining Your Vision ||
# Business Model Canvas
# Business Model Patterns
# Business Model Environment
# Business Model Testing
# Minimum-Viable Product
# Product Roadmap
|}
== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The purpose of the course is to walk students through the concrete steps that are necessary for an entrepreneur to develop a tech product and build a solid business around that tech product.

=== ILOs defined at three levels ===

==== Level 1: What concepts should a student know/remember/explain? ====
By the end of the course, the students should be able to ...
* design-thinking tools to design the prototype of the product,
* approaches to designing and testing a business model through the experiments,
* frameworks of agile development,
* storytelling methods to design a brand,
* pitching presentation tools.

==== Level 2: What basic practical skills should a student be able to perform? ====
By the end of the course, the students should be able to ...
* concrete steps of creating a value proposition for a customer,
* concrete steps of the business design (business model, hypothesis formulation/testing and minimum-viable product creation),
* SCRUM roles, ceremonies and artefacts,
* specifics of pitch presentation for investors.

==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
By the end of the course, the students should be able to ...
* build and manage the startup team,
* define the customer problem and validate it,
* create the product to fit the problem with agile methods,
* define the business model around the product,
* promote a product and a startup,
* build strong networks in the business world.
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 90-100 || -
|-
| B. Good || 75-89 || -
|-
| C. Satisfactory || 60-74 || -
|-
| D. Fail || 0-59 || -
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Final presentation || 30
|-
| Project Report || 20
|-
| Project Progress || 50
|}

=== Recommendations for students on how to succeed in the course ===
Participation is important. Showing up and participating in discussions is the key to success in this course. Students work in teams, so coordinating teamwork will be an important factor for success. Reading the provided materials is mandatory, as lectures will mainly consist of discussions. The main assignment in the course is Market research paper which is supposed to be useful not only for this course but s a basis for future business oriented courses

== Resources, literature and reference materials ==

=== Open access resources ===
* Tidd, J. & Bessant, J. (2011). Managing Innovation: Integrating Technological, Market and Organizational Change
* Stickdorn, M. & Schneider, J. (2010). This is Service Design Thinking. Wiley.
* Brown, T. & Kātz, B. (2009). Change by design. New York: Harper Business.
* Osterwalder, A.& Pigneur, Y. (2010). Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers
* Sutherland, J. (2014). Scrum: The Art of Doing Twice the Work in Half the Time
* Ries, E. (2011). The Lean Startup

=== Closed access resources ===
* N/A

=== Software and tools used within the course ===
* Boardofinnovation.com
* Miro.com
* Notion.com
* MS Teams

= Teaching Methodology: Methods, techniques, & activities =

== Activities and Teaching Methods ==
{| class="wikitable"
|+ Teaching and Learning Methods within each section
|-
! Teaching Techniques !! Section 1 !! Section 2 !! Section 3
|-
| Problem-based learning (students learn by solving open-ended problems without a strictly-defined solution) || 1 || 1 || 1
|-
| Project-based learning (students work on a project) || 1 || 1 || 1
|-
| Differentiated learning (provide tasks and activities at several levels of difficulty to fit students needs and level) || 1 || 1 || 1
|-
| Contextual learning (activities and tasks are connected to the real world to make it easier for students to relate to them); || 1 || 1 || 1
|-
| Business game (learn by playing a game that incorporates the principles of the material covered within the course). || 1 || 1 || 1
|-
| Task-based learning || 1 || 1 || 1
|}
{| class="wikitable"
|+ Activities within each section
|-
! Learning Activities !! Section 1 !! Section 2 !! Section 3
|-
| Lectures || 1 || 1 || 1
|-
| Interactive Lectures || 1 || 1 || 1
|-
| Lab exercises || 1 || 1 || 1
|-
| Cases studies || 1 || 1 || 1
|-
| Group projects || 1 || 1 || 1
|-
| Peer Review || 1 || 0 || 0
|-
| Discussions || 1 || 1 || 1
|-
| Presentations by students || 1 || 1 || 1
|-
| Written reports || 1 || 1 || 1
|-
| Oral Reports || 1 || 1 || 1
|-
| Quizzes (written or computer based) || 0 || 1 || 0
|-
| Simulations and role-plays || 0 || 1 || 0
|-
| Essays || 0 || 1 || 1
|-
| Experiments || 0 || 0 || 1
|-
| Individual Projects || 0 || 0 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Discussion || 1. What is a startup? 2. What are the roles within a team? 3. How should you form the team of a startup? 4. What types of leadership are the most effective? 5. What are the ceremonies, roles and artifacts of SCRUM? || 0
|-
| Workshop || Fill in the team canvas to put all your goals and common values on one page. || 1
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Workshop || 1. Define INTERESTING industries for all team members. Define industries in which you HAVE KNOWLEDGE AND EXPERIENCE. Put these industries on the matrix. Choose ONE industry for your project that meets 2 criteria above. 2. Brainstorm about stakeholders from your market. Choose the segment that you sympathise the most. 3. Define the customer segment you empathise the most (i.e. elderly people, children, office workers etc.). Define JOBS TO BE DONE. Put each job on the separate sticker. Define user's PAINS. Put each pain on the separate sticker.Define user's GAINS. Put each gain on the separate sticker. 4. Brainstorm what products you can offer to the chosen segment with their pains or gains. If you are stuck, use SCAMPER techniques.Group ideas that have the similar topic into clusters. Choose 1 top idea for further development based on 2 defined criteria (innovative potential and feasibility). 5. Choose the best product idea. Define PRODUCTS & SERVICES. Put each item on the separate sticker. Define GAIN CREATORS. Put each item on the separate sticker. Define PAIN RELIEVERS. Put each item on the separate sticker. 6. Review your pain relievers and gain creators.Check if pain relievers and gain creators correspond with JBDs, pains and gains from the customer profile. Highlight those that correspond with each other. If there are any pain relievers and gain creators are left, they don't create the value for a customer. Check how you can redefine you value proposition. 7. Define your 5 main competitors. Define competing factors (these are your pain relievers and gain creators). Draw the strategic canvas based on competing factors. Define areas where you can compete. Redefine your value proposition if necessary (make new priorities for product and services, pain relievers, gain creators. || 0
|-
| Discussion || 1. How to validate a problem? 2. How to validate a market? 3. How to validate a solution? || 0
|-
| Customer research || 1. How customers do their jobs in the industry right now? 2. How can we develop the empathy with users? 3. What is a persona? How to design a persona? || 1
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Discussion || What is the value of the business model canvas by Alexander Osterwalder? What are the components of the business model? What is the Minimum Viable Product (MVP)? How to define must-have, should-have and could-have requirements? || 0
|-
| Group project || Please, develop the business model for your tech product. Please, test your business model using experiments with your prototypes. Please, create the concept for your Minimum Viable Product. || 1
|-
| Workshop || Formulate all blocks of the business model for your business idea. Define the forces that shape your business environment. Define must-have, should-have and could have requirements for your product. || 0
|-
| Group presentation || Create a story for your product. Think about your user as a hero and your product as a helper. || 1
|}
=== Final assessment ===
'''Section 1'''
#

For the final assessment, students should complete the Market Research paper.
It should follow the market research paper structure, contain information about market volume (TAM SAM SOM), data must be gathered with help of data sources learnt.
The paper should refer to market potential and give the basis to make business decisions, answer questions on how to start and develop your idea, what is your business model, target customer persona, product MVP etc.

Grading criteria for the final project presentation:
Market sizing has been carried out
Customer segments are named
Сompetitor analysis has been conducted
At least 2 prominent data sources are used
Customer discovery interviews conducted
Future steps are mapped out
The final report is visualized clearly and transparent

'''Section 2'''

'''Section 3'''

=== The retake exam ===
The retake exam.
For the retake, students have to submit the results of the market sizing exercise with the TAM SAM SOM method in the form of a visual framework studied.

MSc: It business start

2022-07-19T15:51:08Z

R.akhmetzyanov: /* The retake exam */

= IT_Business_Start =
* '''Course name''': IT_Business_Start
* '''Code discipline''':
* '''Subject area''': Technological entrepreneurship

== Short Description ==
This course is for the first-time entrepreneur. We will briefly but concisely discuss all the issues related to starting your own project from scratch: how to make sure that your idea is in demand, how to do market research, how to stop putting off the launch, why the customer is more important than the product, and how to do customer research. During this course, students will get used to their entrepreneurial role, build teams, formulate a business and product idea and be ready to delve into the complexities of business development in the following courses.

== Prerequisites ==

=== Prerequisite subjects ===
* N/A

=== Prerequisite topics ===
* N/A

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Introduction & Building Your Team & Making Your Team Agile ||
# Defining a startup
# Formulating the group project: team, business idea
# Leadership
# Forming the team
# Managing the team
|-
| Defining Your Customer & Defining Your Product & Defining Your Rivals ||
# Customer Segmentation
# Customer Profile (JTBD, Pains, Gains)
# Creating a Value Proposition
# Matching Value Proposition with Customer Profile
# Strategy Canvas
|-
| Defining Your Business Model & Defining Your Vision ||
# Business Model Canvas
# Business Model Patterns
# Business Model Environment
# Business Model Testing
# Minimum-Viable Product
# Product Roadmap
|}
== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The purpose of the course is to walk students through the concrete steps that are necessary for an entrepreneur to develop a tech product and build a solid business around that tech product.

=== ILOs defined at three levels ===

==== Level 1: What concepts should a student know/remember/explain? ====
By the end of the course, the students should be able to ...
* design-thinking tools to design the prototype of the product,
* approaches to designing and testing a business model through the experiments,
* frameworks of agile development,
* storytelling methods to design a brand,
* pitching presentation tools.

==== Level 2: What basic practical skills should a student be able to perform? ====
By the end of the course, the students should be able to ...
* concrete steps of creating a value proposition for a customer,
* concrete steps of the business design (business model, hypothesis formulation/testing and minimum-viable product creation),
* SCRUM roles, ceremonies and artefacts,
* specifics of pitch presentation for investors.

==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
By the end of the course, the students should be able to ...
* build and manage the startup team,
* define the customer problem and validate it,
* create the product to fit the problem with agile methods,
* define the business model around the product,
* promote a product and a startup,
* build strong networks in the business world.
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 90-100 || -
|-
| B. Good || 75-89 || -
|-
| C. Satisfactory || 60-74 || -
|-
| D. Fail || 0-59 || -
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Final presentation || 30
|-
| Project Report || 20
|-
| Project Progress || 50
|}

=== Recommendations for students on how to succeed in the course ===
Participation is important. Showing up and participating in discussions is the key to success in this course. Students work in teams, so coordinating teamwork will be an important factor for success. Reading the provided materials is mandatory, as lectures will mainly consist of discussions. The main assignment in the course is Market research paper which is supposed to be useful not only for this course but s a basis for future business oriented courses

== Resources, literature and reference materials ==

=== Open access resources ===
* Tidd, J. & Bessant, J. (2011). Managing Innovation: Integrating Technological, Market and Organizational Change
* Stickdorn, M. & Schneider, J. (2010). This is Service Design Thinking. Wiley.
* Brown, T. & Kātz, B. (2009). Change by design. New York: Harper Business.
* Osterwalder, A.& Pigneur, Y. (2010). Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers
* Sutherland, J. (2014). Scrum: The Art of Doing Twice the Work in Half the Time
* Ries, E. (2011). The Lean Startup

=== Closed access resources ===
* N/A

=== Software and tools used within the course ===
* Boardofinnovation.com
* Miro.com
* Notion.com
* MS Teams
= Teaching Methodology: Methods, techniques, & activities =

== Activities and Teaching Methods ==
{| class="wikitable"
|+ Teaching and Learning Methods within each section
|-
! Teaching Techniques !! Section 1 !! Section 2 !! Section 3
|-
| Problem-based learning (students learn by solving open-ended problems without a strictly-defined solution) || 1 || 1 || 1
|-
| Project-based learning (students work on a project) || 1 || 1 || 1
|-
| Differentiated learning (provide tasks and activities at several levels of difficulty to fit students needs and level) || 1 || 1 || 1
|-
| Contextual learning (activities and tasks are connected to the real world to make it easier for students to relate to them); || 1 || 1 || 1
|-
| Business game (learn by playing a game that incorporates the principles of the material covered within the course). || 1 || 1 || 1
|-
| Task-based learning || 1 || 1 || 1
|}
{| class="wikitable"
|+ Activities within each section
|-
! Learning Activities !! Section 1 !! Section 2 !! Section 3
|-
| Lectures || 1 || 1 || 1
|-
| Interactive Lectures || 1 || 1 || 1
|-
| Lab exercises || 1 || 1 || 1
|-
| Cases studies || 1 || 1 || 1
|-
| Group projects || 1 || 1 || 1
|-
| Peer Review || 1 || 0 || 0
|-
| Discussions || 1 || 1 || 1
|-
| Presentations by students || 1 || 1 || 1
|-
| Written reports || 1 || 1 || 1
|-
| Oral Reports || 1 || 1 || 1
|-
| Quizzes (written or computer based) || 0 || 1 || 0
|-
| Simulations and role-plays || 0 || 1 || 0
|-
| Essays || 0 || 1 || 1
|-
| Experiments || 0 || 0 || 1
|-
| Individual Projects || 0 || 0 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Discussion || 1. What is a startup? 2. What are the roles within a team? 3. How should you form the team of a startup? 4. What types of leadership are the most effective? 5. What are the ceremonies, roles and artifacts of SCRUM? || 0
|-
| Workshop || Fill in the team canvas to put all your goals and common values on one page. || 1
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Workshop || 1. Define INTERESTING industries for all team members. Define industries in which you HAVE KNOWLEDGE AND EXPERIENCE. Put these industries on the matrix. Choose ONE industry for your project that meets 2 criteria above. 2. Brainstorm about stakeholders from your market. Choose the segment that you sympathise the most. 3. Define the customer segment you empathise the most (i.e. elderly people, children, office workers etc.). Define JOBS TO BE DONE. Put each job on the separate sticker. Define user's PAINS. Put each pain on the separate sticker.Define user's GAINS. Put each gain on the separate sticker. 4. Brainstorm what products you can offer to the chosen segment with their pains or gains. If you are stuck, use SCAMPER techniques.Group ideas that have the similar topic into clusters. Choose 1 top idea for further development based on 2 defined criteria (innovative potential and feasibility). 5. Choose the best product idea. Define PRODUCTS & SERVICES. Put each item on the separate sticker. Define GAIN CREATORS. Put each item on the separate sticker. Define PAIN RELIEVERS. Put each item on the separate sticker. 6. Review your pain relievers and gain creators.Check if pain relievers and gain creators correspond with JBDs, pains and gains from the customer profile. Highlight those that correspond with each other. If there are any pain relievers and gain creators are left, they don't create the value for a customer. Check how you can redefine you value proposition. 7. Define your 5 main competitors. Define competing factors (these are your pain relievers and gain creators). Draw the strategic canvas based on competing factors. Define areas where you can compete. Redefine your value proposition if necessary (make new priorities for product and services, pain relievers, gain creators. || 0
|-
| Discussion || 1. How to validate a problem? 2. How to validate a market? 3. How to validate a solution? || 0
|-
| Customer research || 1. How customers do their jobs in the industry right now? 2. How can we develop the empathy with users? 3. What is a persona? How to design a persona? || 1
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Discussion || What is the value of the business model canvas by Alexander Osterwalder? What are the components of the business model? What is the Minimum Viable Product (MVP)? How to define must-have, should-have and could-have requirements? || 0
|-
| Group project || Please, develop the business model for your tech product. Please, test your business model using experiments with your prototypes. Please, create the concept for your Minimum Viable Product. || 1
|-
| Workshop || Formulate all blocks of the business model for your business idea. Define the forces that shape your business environment. Define must-have, should-have and could have requirements for your product. || 0
|-
| Group presentation || Create a story for your product. Think about your user as a hero and your product as a helper. || 1
|}
=== Final assessment ===
'''Section 1'''
#

For the final assessment, students should complete the Market Research paper.
It should follow the market research paper structure, contain information about market volume (TAM SAM SOM), data must be gathered with help of data sources learnt.
The paper should refer to market potential and give the basis to make business decisions, answer questions on how to start and develop your idea, what is your business model, target customer persona, product MVP etc.

Grading criteria for the final project presentation:
Market sizing has been carried out
Customer segments are named
Сompetitor analysis has been conducted
At least 2 prominent data sources are used
Customer discovery interviews conducted
Future steps are mapped out
The final report is visualized clearly and transparent

'''Section 2'''

'''Section 3'''

=== The retake exam ===
The retake exam.
For the retake, students have to submit the results of the market sizing exercise with the TAM SAM SOM method in the form of a visual framework studied.

MSc: Venture and growth hacking

2022-07-19T15:42:26Z

R.akhmetzyanov: /* Course grading range */

= Venture_and_Growth_Hacking =
* '''Course name''': Venture_and_Growth_Hacking
* '''Code discipline''': CSE807
* '''Subject area''': Software Engineering

== Short Description ==
This course explores foundations for Venture Capital and provides the formation and development of knowledge, skills and abilities to attract investors, creating compelling presentations, closing deals to raise capital, and maintaining relationships with venture capital and other investors.

== Prerequisites ==

=== Prerequisite subjects ===
* None

=== Prerequisite topics ===
* Existing startup, startup idea or desire to quickly develop one
* Interest and some basic knowledge on what is a startup and some successful startups and their stories of fundraising (Dropbox, Airbnb, Uber, Facebook, Pebble Smart Watch, )
* Basic math and accounting/finances skills to calculate cap table, dilution, company valuation and so on
* Soft and hard skills of a founder
* Presentation (oral and written) skills (English)

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Startup life cycle and types of investors ||
# Basic concepts, rounds and types of investors
# Metrics for startups
# Role venture capital in business and economic development
|-
| Venture capital: types of transactions and fundraising ||
# Why does a startup need venture capital?
# Searching and attracting of investors
# Relationship maintenance and preparation for subsequent rounds
# Criteria of a successful startup for investment purposes
# key metrics for tech/IT startup
|-
| Company valuation and financial planning ||
# Company valuation (template provided)
# Estimating the size of the company's market and its growth potential
# Creation of a cap table for seed -> series A stages (template provided)
|-
| Fundraising practice ||
# Investment presentation, feedback, iteration
# Coaching
# Where to find investors, how to approach them and maintain relationships
# Formulating an investment request
# Legal support and conditions (term sheet)
# Negotiation practice, strategy and case-based negotiation game among students
|}
== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to develop an understanding of how start-ups or start-ups are financed, what types of investments are available, and what the business financing process entails. Also: develop practical skills in managing the financing process from the point of view of both an entrepreneur and an investor.

=== ILOs defined at three levels ===

==== Level 1: What concepts should a student know/remember/explain? ====
By the end of the course, the students should be able to ...
* Know the difference between convertible note, SAFE, and series A/B/C
* Explain where to form a legal entity correctly so as not to scare Western investors
* Describe when a lawyer is needed
* Elaborate purpose, benefits and harms of a non-disclosure agreement
* Elaborate key metrics and indicators for various types of projects

==== Level 2: What basic practical skills should a student be able to perform? ====
By the end of the course, the students should be able to ...
* The difference between types of investors
* How and why different types of investors work
* The structure of a good project presentation (startup pitch)
* How and why to develop an effective story about a project in an elevator (elevator pitch)
* What metrics and data are important to investors

==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
By the end of the course, the students should be able to ...
* Create a presentation of the project
* Create, establish and maintain efficient relationships with favorable investors
* Understand VC terminology
* Find and attract angel and VC investors for their ventures (startup projects)
* Negotiate fundraising deals
* Create and leverage the right startup metrics
* Listen to feedback and incorporate it into refining your presentation, product, and startup management.
* Work with investors and collaborate with them
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| Pass || 80-100 || -
|-
| Fail || 0-79 || -
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Paper #0: Testing by methods of assessing the value of a company || 20
|-
| Paper #1: Written work: formulate an investment request || 20
|-
| Paper #2: Public presentation || 60
|}

=== Recommendations for students on how to succeed in the course ===
It is highly recommended that you treat the course writing as a tool to help you make decisions about growing your business. The purpose of studying the discipline is the practice of interacting with potential investors, the classes welcome a proactive approach to creating and maintaining relationships with university partners, requesting contacts and meetings with representatives of venture funds Participation is important. Showing up is the key to success in this course. You will work in teams, so coordinating teamwork will be an important factor for success.

== Resources, literature and reference materials ==

=== Open access resources ===
* Jackson, Michael. "The world and the machine." ICSE '95: Proceedings of the 17th international conference on Software engineeringApril 1995 Pages 283–292,
* The Guide to Product Metrics:

=== Closed access resources ===
* Ramsinghani M. The business of venture capital: insights from leading practitioners on the art of raising a fund, deal structuring, value creation, and exit strategies. – John Wiley & Sons, 2014.
* Feld B., Mendelson J. Venture deals: Be smarter than your lawyer and venture capitalist. – John Wiley & Sons, 2019.
* Michalowicz M. Profit First: Ein einfaches System, jedwedes Unternehmen von einem kapital fressenden Monster in eine Geldmaschine zu verwandeln. – Verlag Barbara Budrich, 2020.
* Aswath D. Investment valuation. – 2016.
* Blank Steven G. The four steps to Epiphany. – 2006.

=== Software and tools used within the course ===
* Google Sheets or MS Excel or similar
* Google Docs or MS Word or similar
= Teaching Methodology: Methods, techniques, & activities =

== Activities and Teaching Methods ==
{| class="wikitable"
|+ Teaching and Learning Methods within each section
|-
! Teaching Techniques !! Section 1 !! Section 2 !! Section 3 !! Section 4
|-
| Problem-based learning (students learn by solving open-ended problems without a strictly-defined solution) || 1 || 1 || 1 || 1
|-
| Project-based learning (students work on a project) || 1 || 1 || 1 || 1
|-
| Differentiated learning (provide tasks and activities at several levels of difficulty to fit students needs and level) || 1 || 1 || 1 || 1
|-
| развивающего обучения (задания и материал "прокачивают" ещё нераскрытые возможности студентов); || 1 || 1 || 1 || 1
|-
| концентрированного обучения (занятия по одной большой теме логически объединяются); || 1 || 1 || 1 || 1
|-
| inquiry-based learning || 1 || 1 || 1 || 1
|}
{| class="wikitable"
|+ Activities within each section
|-
! Learning Activities !! Section 1 !! Section 2 !! Section 3 !! Section 4
|-
| Lectures || 1 || 1 || 1 || 1
|-
| Interactive Lectures || 1 || 1 || 1 || 1
|-
| Lab exercises || 1 || 1 || 1 || 1
|-
| Development of individual parts of software product code || 1 || 1 || 1 || 1
|-
| Group projects || 1 || 1 || 1 || 1
|-
| Peer Review || 1 || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1 || 1
|-
| Presentations by students || 1 || 1 || 1 || 1
|-
| Written reports || 0 || 1 || 1 || 1
|-
| Experiments || 0 || 0 || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Quiz || 1. Why do you need to know the value of a company? 2. What type of investor might be interested in a company in your stage of development? 3. Venture rounds, their relationship with the stage of development of the company. 4. Types of venture investors and their features of acceptance decisions. 5. Investment cycle: stages, process, preparation, documents. || 1
|-
| Individual Assignments || Make a portrait and database of relevant venture investors for your company || 1
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Quiz || 1. How to convey financial information about your company to stakeholders and investors? 2. How to convey financial indicators to the investor and justify the required amount of capital for growth? 3. How to calculate financial indicators based on reporting and assess the financial health of the company? 4. What could be the future consequences of overvaluing or undervaluing your startup? || 1
|-
| Individual Assignments || Formulate an investment request for your company (or justify the lack of need for venture funds) || 1
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Quiz || 1. How much capital should your company raise based on growth prospects? 2. Which financial ratios of the company should be improved and how? 3. How much capital should your company raise based on growth prospects? 4. What is a balance sheet and what operations are shown in this financial report? 5. What is an income statement and what transactions are shown in this financial statement? How does it relate to the balance sheet? 6. What is a cash flow statement and why is it needed? 7. How do investments, debt, sales, cash outflows affect the financial statement? || 1
|-
| Presentation || 1. Assess the market potential of your project 2. Formulate and justify an investment request || 1
|}
==== Section 4 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Quiz || 1. How do you determine if certain investor conditions are unfair? 2. What might be the typical future consequences of specific conditions and undervaluation or overvaluation of your company? 3. Strategies for Negotiating Better Deals and Maintaining Control of the Company 4. How does subsequent investment dilute your ownership? || 1
|-
| Individual Assignments || 1. Develop a strategy for negotiating with the investor about your project 2. Write a welcome letter to the investor 3. Prepare an investment presentation and present it publicly || 1
|-
| Group Project Work || 1. Build an investor negotiation strategy 2. Justify the investment potential of your business 3. Prepare a financial plan for the development of your company for distribution to investors || 1
|}
=== Final assessment ===
'''Section 1'''
# Grading criteria for the final project presentation:
# The difference between types of investors
# What metrics and data are important to investors?
'''Section 2'''
# Legal justification and main ways of doing business
# When you need a lawyer
# Economic, legal and patrimonial errors when closing deals Negotiation
# How the financial performance of a startup is related to an investment round
# Why is VC the Riskiest Asset class?
'''Section 3'''
# Main metrics and indicators for various types of projects
# Market Growth and Potential Studies
# Calculating the Total Available Market
'''Section 4'''
# Decision Criteria about the deal
# Tactics of negotiations with an investor
# Structure and content of an investment presentation
# Strategies to maintain control over your company

=== The retake exam ===

The retake exam is the same - pitch deck presentation. For absence from the lectures or lack of activity during the lectures, 10-20 points will be subtracted from the score to achieve the grade.

MSc: Venture and growth hacking

2022-07-19T15:34:55Z

R.akhmetzyanov: /* The retake exam */

= Venture_and_Growth_Hacking =
* '''Course name''': Venture_and_Growth_Hacking
* '''Code discipline''': CSE807
* '''Subject area''': Software Engineering

== Short Description ==
This course explores foundations for Venture Capital and provides the formation and development of knowledge, skills and abilities to attract investors, creating compelling presentations, closing deals to raise capital, and maintaining relationships with venture capital and other investors.

== Prerequisites ==

=== Prerequisite subjects ===
* None

=== Prerequisite topics ===
* Existing startup, startup idea or desire to quickly develop one
* Interest and some basic knowledge on what is a startup and some successful startups and their stories of fundraising (Dropbox, Airbnb, Uber, Facebook, Pebble Smart Watch, )
* Basic math and accounting/finances skills to calculate cap table, dilution, company valuation and so on
* Soft and hard skills of a founder
* Presentation (oral and written) skills (English)

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Startup life cycle and types of investors ||
# Basic concepts, rounds and types of investors
# Metrics for startups
# Role venture capital in business and economic development
|-
| Venture capital: types of transactions and fundraising ||
# Why does a startup need venture capital?
# Searching and attracting of investors
# Relationship maintenance and preparation for subsequent rounds
# Criteria of a successful startup for investment purposes
# key metrics for tech/IT startup
|-
| Company valuation and financial planning ||
# Company valuation (template provided)
# Estimating the size of the company's market and its growth potential
# Creation of a cap table for seed -> series A stages (template provided)
|-
| Fundraising practice ||
# Investment presentation, feedback, iteration
# Coaching
# Where to find investors, how to approach them and maintain relationships
# Formulating an investment request
# Legal support and conditions (term sheet)
# Negotiation practice, strategy and case-based negotiation game among students
|}
== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to develop an understanding of how start-ups or start-ups are financed, what types of investments are available, and what the business financing process entails. Also: develop practical skills in managing the financing process from the point of view of both an entrepreneur and an investor.

=== ILOs defined at three levels ===

==== Level 1: What concepts should a student know/remember/explain? ====
By the end of the course, the students should be able to ...
* Know the difference between convertible note, SAFE, and series A/B/C
* Explain where to form a legal entity correctly so as not to scare Western investors
* Describe when a lawyer is needed
* Elaborate purpose, benefits and harms of a non-disclosure agreement
* Elaborate key metrics and indicators for various types of projects

==== Level 2: What basic practical skills should a student be able to perform? ====
By the end of the course, the students should be able to ...
* The difference between types of investors
* How and why different types of investors work
* The structure of a good project presentation (startup pitch)
* How and why to develop an effective story about a project in an elevator (elevator pitch)
* What metrics and data are important to investors

==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
By the end of the course, the students should be able to ...
* Create a presentation of the project
* Create, establish and maintain efficient relationships with favorable investors
* Understand VC terminology
* Find and attract angel and VC investors for their ventures (startup projects)
* Negotiate fundraising deals
* Create and leverage the right startup metrics
* Listen to feedback and incorporate it into refining your presentation, product, and startup management.
* Work with investors and collaborate with them
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| Pass || 80-100 || -
|-
| Fail || 0-79 || Pass
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Paper #0: Testing by methods of assessing the value of a company || 20
|-
| Paper #1: Written work: formulate an investment request || 20
|-
| Paper #2: Public presentation || 60
|}

=== Recommendations for students on how to succeed in the course ===
It is highly recommended that you treat the course writing as a tool to help you make decisions about growing your business. The purpose of studying the discipline is the practice of interacting with potential investors, the classes welcome a proactive approach to creating and maintaining relationships with university partners, requesting contacts and meetings with representatives of venture funds Participation is important. Showing up is the key to success in this course. You will work in teams, so coordinating teamwork will be an important factor for success.

== Resources, literature and reference materials ==

=== Open access resources ===
* Jackson, Michael. "The world and the machine." ICSE '95: Proceedings of the 17th international conference on Software engineeringApril 1995 Pages 283–292,
* The Guide to Product Metrics:

=== Closed access resources ===
* Ramsinghani M. The business of venture capital: insights from leading practitioners on the art of raising a fund, deal structuring, value creation, and exit strategies. – John Wiley & Sons, 2014.
* Feld B., Mendelson J. Venture deals: Be smarter than your lawyer and venture capitalist. – John Wiley & Sons, 2019.
* Michalowicz M. Profit First: Ein einfaches System, jedwedes Unternehmen von einem kapital fressenden Monster in eine Geldmaschine zu verwandeln. – Verlag Barbara Budrich, 2020.
* Aswath D. Investment valuation. – 2016.
* Blank Steven G. The four steps to Epiphany. – 2006.

=== Software and tools used within the course ===
* Google Sheets or MS Excel or similar
* Google Docs or MS Word or similar
= Teaching Methodology: Methods, techniques, & activities =

== Activities and Teaching Methods ==
{| class="wikitable"
|+ Teaching and Learning Methods within each section
|-
! Teaching Techniques !! Section 1 !! Section 2 !! Section 3 !! Section 4
|-
| Problem-based learning (students learn by solving open-ended problems without a strictly-defined solution) || 1 || 1 || 1 || 1
|-
| Project-based learning (students work on a project) || 1 || 1 || 1 || 1
|-
| Differentiated learning (provide tasks and activities at several levels of difficulty to fit students needs and level) || 1 || 1 || 1 || 1
|-
| развивающего обучения (задания и материал "прокачивают" ещё нераскрытые возможности студентов); || 1 || 1 || 1 || 1
|-
| концентрированного обучения (занятия по одной большой теме логически объединяются); || 1 || 1 || 1 || 1
|-
| inquiry-based learning || 1 || 1 || 1 || 1
|}
{| class="wikitable"
|+ Activities within each section
|-
! Learning Activities !! Section 1 !! Section 2 !! Section 3 !! Section 4
|-
| Lectures || 1 || 1 || 1 || 1
|-
| Interactive Lectures || 1 || 1 || 1 || 1
|-
| Lab exercises || 1 || 1 || 1 || 1
|-
| Development of individual parts of software product code || 1 || 1 || 1 || 1
|-
| Group projects || 1 || 1 || 1 || 1
|-
| Peer Review || 1 || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1 || 1
|-
| Presentations by students || 1 || 1 || 1 || 1
|-
| Written reports || 0 || 1 || 1 || 1
|-
| Experiments || 0 || 0 || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Quiz || 1. Why do you need to know the value of a company? 2. What type of investor might be interested in a company in your stage of development? 3. Venture rounds, their relationship with the stage of development of the company. 4. Types of venture investors and their features of acceptance decisions. 5. Investment cycle: stages, process, preparation, documents. || 1
|-
| Individual Assignments || Make a portrait and database of relevant venture investors for your company || 1
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Quiz || 1. How to convey financial information about your company to stakeholders and investors? 2. How to convey financial indicators to the investor and justify the required amount of capital for growth? 3. How to calculate financial indicators based on reporting and assess the financial health of the company? 4. What could be the future consequences of overvaluing or undervaluing your startup? || 1
|-
| Individual Assignments || Formulate an investment request for your company (or justify the lack of need for venture funds) || 1
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Quiz || 1. How much capital should your company raise based on growth prospects? 2. Which financial ratios of the company should be improved and how? 3. How much capital should your company raise based on growth prospects? 4. What is a balance sheet and what operations are shown in this financial report? 5. What is an income statement and what transactions are shown in this financial statement? How does it relate to the balance sheet? 6. What is a cash flow statement and why is it needed? 7. How do investments, debt, sales, cash outflows affect the financial statement? || 1
|-
| Presentation || 1. Assess the market potential of your project 2. Formulate and justify an investment request || 1
|}
==== Section 4 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Quiz || 1. How do you determine if certain investor conditions are unfair? 2. What might be the typical future consequences of specific conditions and undervaluation or overvaluation of your company? 3. Strategies for Negotiating Better Deals and Maintaining Control of the Company 4. How does subsequent investment dilute your ownership? || 1
|-
| Individual Assignments || 1. Develop a strategy for negotiating with the investor about your project 2. Write a welcome letter to the investor 3. Prepare an investment presentation and present it publicly || 1
|-
| Group Project Work || 1. Build an investor negotiation strategy 2. Justify the investment potential of your business 3. Prepare a financial plan for the development of your company for distribution to investors || 1
|}
=== Final assessment ===
'''Section 1'''
# Grading criteria for the final project presentation:
# The difference between types of investors
# What metrics and data are important to investors?
'''Section 2'''
# Legal justification and main ways of doing business
# When you need a lawyer
# Economic, legal and patrimonial errors when closing deals Negotiation
# How the financial performance of a startup is related to an investment round
# Why is VC the Riskiest Asset class?
'''Section 3'''
# Main metrics and indicators for various types of projects
# Market Growth and Potential Studies
# Calculating the Total Available Market
'''Section 4'''
# Decision Criteria about the deal
# Tactics of negotiations with an investor
# Structure and content of an investment presentation
# Strategies to maintain control over your company

=== The retake exam ===

The retake exam is the same - pitch deck presentation. For absence from the lectures or lack of activity during the lectures, 10-20 points will be subtracted from the score to achieve the grade.

MSTE: Ceo toolkit

2022-07-19T15:19:56Z

R.akhmetzyanov: /* CEO_Toolkit */

= CEO Toolkit: Strategy, Operations, Finance, HR, Legal Aspects, IP Law and Innovations =
* '''Course name''': CEO_Toolkit
* '''Code discipline''':
* '''Subject area''': Technology Entrepreneurship

== Short Description ==
The course is made to give a brief knowledge and skills in practical running a company in an actual environment. It is about practical management for a CEO, starting from team building, product and project management, registering a company and running it to managing growth and exiting a business by the end.
The course helps to understand the whole process and systematize other management and marketing topics. It would be helpful for startup founder, CEO or manager to better plan practical activities and lower the risks of running a company.
The course is based on the principles of project-based learning and connecting theory and practice. Based on these principles, the course uses lectures to a lesser extent, more practice followed by group and individual work, as well as discussions, analysis of business cases and learning by doing.

== Prerequisites ==

=== Prerequisite subjects ===
* HSS321

=== Prerequisite topics ===
* Corporate legal support, taxation
* History of management
* Strategic management
* Operational management
* Financial planning
* Marketing-management
* Team building and HR
* Innovations
* Intellectual property

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Corporate legal support, taxation ||
# Legal entity types and differences
# Fundamentals of taxation for small businesses in Russia
# Possibilities of international jurisdictions
# Legal support of transactions with a client: competent document management
|-
| Introduction to management and marketing as a sphere of knowledge, skills and art ||
# What is management and its functions
# History of management and marketing as a practical knowledge and science
|-
| Strategic management ||
# Organisation types
# Life-cycle of an organization
# Strategic management concepts and frameworks
# Strategic planning
# Strategic management as a function of management
|-
| Operational management ||
# Leader types and management styles
# Stages of development of the organization
# Differences between a startup model and a small business
# Choice of operational strategy
# Value Management
# Project management
# Operational planning
# Operational control and optimization of business processes
# Communications in operational management
|-
| Financial management and planning ||
# Financial accounting, management and planning, accounting tools
# Required financial statements for a startup: cash flow statement, income statement, balance sheet
# Cross-border financial management
# Basic metrics of the unit economy of a startup
|-
| Marketing management ||
# Basic concepts of marketing
# Marketing functions and its place in organization management
# Marketing mix
# Marketing strategy
|-
| HR, Team building and hiring ||
# Authority and leadership
# Organizational culture
# Group dynamics and organizational changes
# An effective team as an element of operational management
# Leadership and management skills, as well as control methods
# Recruiting, motivating and retaining the project team
# Human resources, human capital and human potential in IT-startups (trends, features, CEO-vision)
# Recruitment and assessment for IT-startups (how CEO can do it himself/herself or check the process of his HR-specialist/partner agency)
# Interview with a candidate: from preparation to evaluation
# Career modeling in IT-startups: career tracks, educational tracks, corporate training.
# Corporate culture
# Organization of remote/online job process for the team
# HR lifehacks for CEO: from “how to hire” to “how to fire”
# Creation of a positive HR-brand
|-
| Innovations ||
# Innovation management
# Managing complex innovation
# Innovation management typologies
# Entrepreneurship in innovation management
|-
| Intellectual property ||
# How to protect IP objects in the field of IT
# How to manage an IP portfolio
# How to assess risks
# Interaction with government agencies: Rospatent, FAS and international agencies
# How to monetize IP
|}
== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to provide the formation and development of the knowledge, skills and abilities necessary for the successful operation of the managing director of a technology company.

=== ILOs defined at three levels ===

==== Level 1: What concepts should a student know/remember/explain? ====
By the end of the course, the students should be able to ...
* Basic concepts of marketing
* Innovation management
* Leader types and management styles;
* Stages of development of the organization;
* Differences between a startup model and a small business;
* Choice of operational strategy;
* Value Management;
* Operational planning;
* Project management;
* An effective team as an element of operational management;
* Operational control and optimization of business processes;
* Fundamentals of financial planning, reports and unit economics of a startup;
* Legal support for the activities of small and medium-sized enterprises;
* The value of intellectual property for a startup, as well as methods for protecting it.

==== Level 2: What basic practical skills should a student be able to perform? ====
By the end of the course, the students should be able to ...
* Build a value chain;
* Read and understand financial statements;
* Build a business process map;
* Choose the best management style for your company;
* Control operational processes;
* Understand basic concepts of marketing.

==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
By the end of the course, the students should be able to ...
* Strategic planning skills;
* Marketing management skills
* Financial planning and accounting skills;
* Understanding the legal and legal support of the company's activities;
* Skills to manage and protect the intellectual property of the company;
* Leadership and management skills, as well as control methods;
* Recruiting, motivating and retaining the project team;
* Implement innovation management skills.
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 75-85 || -
|-
| C. Satisfactory || 60-74 || -
|-
| D. Fail || 0-59 || -
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Weekly student reports || 20
|-
| Business cases || 20
|-
| Labs || 20
|-
| Multiple Choice Testing || 20
|-
| Oral exam || 20
|}

=== Recommendations for students on how to succeed in the course ===
Work out lecture notes. Work out the materials of seminars (practical) classes. In case of difficulty, formulate questions to the teacher. To prepare for the classes, it is recommended to use the presented sources in electronic formats and additional literature. Students during the course work in teams and perform tasks jointly. Teams of 2 to 5 people are allowed. It is also possible to work with a team that is not part of the study group.

== Resources, literature and reference materials ==

=== Open access resources ===
* Peter F. Drucker (2011) The Practice of Management. Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN 711 Third Avenue, New York, NY 10017, USA.
* Philip Kotler, Kevin Lane Keller, Mairead Brady, Malcolm Goodman, Torben Hansen (2019)
* Iacocca, Lee A; Whitney, Catherine (2007), Scribner, New York:
* Tony Hsieh (2013) Delivering Happiness: A Path to Profits, Passion, and Purpose,
* Emotional Intelligence for Project Managers: The People Skills You Need to Achieve Outstanding Results, Paperback
* W. Chan Kim and Renée Mauborgne (2015), Blue Ocean Strategy, Harvard Business School Press
* Jason Fryde (2010), David Heinmeyer Hansson Rework: Business without prejudice Crown Business,
* Ichak Adizes (2014) Managing Corporate Lifecycles (Pb): How Organizations Grow, Age And Die, Embassy Books
* Goldratt's Theory of Constraints: A Systems Approach to Continuous Improvement, Asq Pr; 1st Edition (March 13, 1997)
* Management
* http://www.gidromet.edu.kh.ua/Files/downloads/%D0%9E%D1%81%D0%BD%D0%BE%D0%B2%D1%8B%20%D0%BC%D0%B5%D0%BD%D0%B5%D0%B4%D0%B6%D0%BC%D0%B5%D0%BD%D1%82%D0%B0.%20%D0%9C%D0%B5%D1%81%D0%BA%D0%BE%D0%BD%D0%9C.,%20%D0%90%D0%BB%D1%8C%D0%B1%D0%B5%D1%80%D1%82%20%D0%9C.,%20%D0%A5%D0%B5%D0%B4%D0%BE%D1%83%D1%80%D0%B8%20%D0%A4.,1997-704%20%D1%81%D1%82%D1%80.pdf

=== Closed access resources ===
* Bass, BM, Bass, R (2008) The Bass Handbook of Leadership: Theory, Research, and Application. New York: Simon & Schuster.
* Naumov, S. A. (2017). Managing Operational Capabilities in Startup Companies. (n.p.): Massachusetts Institute of Technology, Sloan School of Management.
* Bhimani, A. (2017). Financial Management for Technology Start-Ups: A Handbook for Growth. Индия: Kogan Page.
* Interiano, L. (2021). Startup Recruitment Guide: Essential Tips For Hiring Employees To Your Business: Guide For Startup Hiring And Company Recruiting Companies. (n.p.): Independently Published.
* Kotter, JP (1990) Force for Change: How Leadership Differs from Management. New York: Simon & Schuster.
* Pearce, CL, Conger, JA (2003) Shared Leadership: Reframing the Hows and Whys of Leadership. Thousand Oaks, CA: SAGE.
* Tormey, P., Tormey, J. (2014). Startup Guide to Intellectual Property: Early Stage Protection of IP. Великобритания: CreateSpace Independent Publishing Platform.
* Brennan, K. (2018). Startup CFO: The Finance Handbook for Your Growing Business. Соединенные Штаты Америки: Amazon Digital Services LLC - KDP Print US.
* Tom DeMarco (1997) The Deadline: A Novel About Project Management

=== Software and tools used within the course ===
* MSTeams License,
* Moodle
* Office software

= Teaching Methodology: Methods, techniques, & activities =

== Activities and Teaching Methods ==
{| class="wikitable"
|+ Teaching and Learning Methods within each section
|-
! Teaching Techniques !! Section 1 !! Section 2 !! Section 3 !! Section 4 !! Section 5 !! Section 6 !! Section 7 !! Section 8 !! Section 9
|-
| Problem-based learning (students learn by solving open-ended problems without a strictly-defined solution) || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Project-based learning (students work on a project) || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Differentiated learning (provide tasks and activities at several levels of difficulty to fit students needs and level) || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Contextual learning (activities and tasks are connected to the real world to make it easier for students to relate to them); || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Business game (learn by playing a game that incorporates the principles of the material covered within the course). || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| inquiry-based learning || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Just-in-time teaching || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Task-based learning || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|}
{| class="wikitable"
|+ Activities within each section
|-
! Learning Activities !! Section 1 !! Section 2 !! Section 3 !! Section 4 !! Section 5 !! Section 6 !! Section 7 !! Section 8 !! Section 9
|-
| Lectures || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Interactive Lectures || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Modeling || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Cases studies || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Individual Projects || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Group projects || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Quizzes (written or computer based) || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Peer Review || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Presentations by students || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Written reports || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Oral Reports || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1 || 1
|-
| Lab exercises || 0 || 1 || 1 || 1 || 0 || 0 || 1 || 1 || 1
|-
| Experiments || 0 || 1 || 1 || 1 || 0 || 0 || 1 || 1 || 1
|-
| Simulations and role-plays || 0 || 0 || 1 || 1 || 0 || 0 || 0 || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Weekly student reports Business cases Labs Multiple Choice Testing || Legal entity types and differences Fundamentals of taxation for small businesses in Russia Possibilities of international jurisdictions Legal support of transactions with a client: competent document management Testing on forms of taxation for small businesses Choosing an adequate legal form for a startup || 1
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Weekly student reports Business cases Labs Multiple Choice Testing || What is management and its functions History of management and marketing as a practical knowledge and science Analyzing and application of management functions || 1
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Weekly student reports Business cases Labs Multiple Choice Testing || Organisation types Life-cycle of an organization Strategic management concepts and frameworks Strategic planning Strategic management as a function of management Testing concepts and planning || 1
|}
==== Section 4 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Weekly student reports Business cases Labs Multiple Choice Testing || How to turn operational tasks into an orderly and transparent process by implementing a system of metrics and continuous analysis? How to manage quality and optimize costs? How to build a system of effective company management and determine business growth points? How to improve competitiveness? How to set long-term goals and define metrics for them. You will be able to prioritize and plan short-term goals. Business case on building a system of metrics and indicators, planning and goal setting for a start-up company. || 1
|}
==== Section 5 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Weekly student reports Business cases Labs Multiple Choice Testing || Financial modeling for a startup: practicing in practice, compiling a financial model of a company || 1
|}
==== Section 6 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Weekly student reports Business cases Labs Multiple Choice Testing || Business case on marketing mix Analyzing marketing strategy of a startup || 1
|}
==== Section 7 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Weekly student reports Business cases Labs Multiple Choice Testing || Exchange of experience, discussion of specifics of hiring in the IT sector || 1
|}
==== Section 8 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Weekly student reports Business cases Labs Multiple Choice Testing || Implementation of innovations in management || 1
|}
==== Section 9 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Weekly student reports Business cases Labs Multiple Choice Testing || Business case on intellectual property protection methods (influence of competitors, conflicts and division of property in a team) || 1
|}
=== Final assessment ===
'''Section 1'''
# What taxation system to choose for an IT startup in Russia
# DOS features for IT companies
# Income tax
# Privileges
# Features of the simplified tax system for the IT-Sphere
# Patent system
# Ways to legally save on taxes
'''Section 2'''
# What is management: definition and its functions
# History of management and marketing as a practical knowledge and science
# Analyzing and application of management functions
'''Section 3'''
# What are organization types?
# Life-cycle of an organization: characteristics
# Strategic planning of a company
# Testing concepts and planning
'''Section 4'''
# How to turn operational tasks into an orderly and transparent process by implementing a system of metrics and continuous analysis?
# How to manage quality and optimize costs?
# How to build a system of effective company management and determine business growth points?
# How to improve competitiveness?
# How to set long-term goals and define metrics for them. You will be able to prioritize and plan short-term goals.
'''Section 5'''
# How to analyze financial statements and foreign market
# How to calculate the unit-economy and determine the profitability of the company
'''Section 6'''
# What are basic concepts of marketing?
# How to create a marketing strategy?
'''Section 7'''
# How to define project goals, calculate the economy, manage people, deadlines and tasks
# How to create and manage teams, even if they work remotely.
# How to manage engagement and motivation.
'''Section 8'''
# What is innovation management?
# How to manage complex innovation?
# Innovation management typologies
# Innovation management in entrepreneurship
'''Section 9'''
# How to protect company innovations
# How to protect the company's intangible assets and keep products competitive.
# Features of registration of trademarks and protection of intellectual property rights
# How to interact with Rospatent and maintain your position in court.

=== The retake exam ===
'''Section 1'''
# What taxation system to choose for an IT startup in Russia
# DOS features for IT companies
# income tax
# Privileges
# Features of the simplified tax system for the IT-Sphere
# patent system
# Ways to legally save on taxes
'''Section 2'''
# What is management: definition and its functions
# History of management and marketing as a practical knowledge and science
# Analyzing and application of management functions
'''Section 3'''
# What are organization types?
# Life-cycle of an organization: characteristics
# Strategic planning of a company
# Testing concepts and planning
'''Section 4'''
# How to turn operational tasks into an orderly and transparent process by implementing a system of metrics and continuous analysis?
# How to manage quality and optimize costs?
# How to build a system of effective company management and determine business growth points?
# How to improve competitiveness?
# How to set long-term goals and define metrics for them. You will be able to prioritize and plan short-term goals.
'''Section 5'''
# How to analyze financial statements and foreign market
# How to calculate the unit-economy and determine the profitability of the company
'''Section 6'''
# What are basic concepts of marketing?
# How to create a marketing strategy?
'''Section 7'''
# How to define project goals, calculate the economy, manage people, deadlines and tasks
# How to create and manage teams, even if they work remotely.
# How to manage engagement and motivation.
'''Section 8'''
# What is innovation management?
# How to manage complex innovation?
# Innovation management typologies
# Innovation management in entrepreneurship
'''Section 9'''
# How to protect company innovations
# How to protect the company's intangible assets and keep products competitive.
# Features of registration of trademarks and protection of intellectual property rights
# How to interact with Rospatent and maintain your position in court.

BSc:Syllabi Index

2022-07-13T13:53:26Z

R.akhmetzyanov: /* Security and Networks */

== Technical ==

=== Computer Science, Programming ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming CSE101 — Introduction to Programming I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming_II CSE102 — Introduction to Programming II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Computer_Science CSE103 — Theoretical Computer Science]
* [https://eduwiki.innopolis.university/index.php/BSc:_Operating_Systems CSE105 — Operating Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Databases CSE106 — Databases]
* [https://eduwiki.innopolis.university/index.php/BSc:_Advanced_Compilers_Construction_and_Program_Analysis CSE108 — Advanced Compilers Construction and Program Analysis]
* [https://eduwiki.innopolis.university/index.php/BSc:_Programming_Paradigms CSE109 — Programming Paradigms]
* [https://eduwiki.innopolis.university/index.php/BSc:_Software_Systems_Design CSE112 — Software Systems Analysis and Design]
* [https://eduwiki.innopolis.university/index.php/BSc:_Distributed_And_Network_Programming CSE114 — Distributed And Network Programming]
* [https://eduwiki.innopolis.university/index.php/BSc:_Compilers_Construction CSE115 — Compilers Construction]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Structures_Algorithms CSE117 — Data Structures and Algorithms]

=== Maths ===
* [https://eduwiki.innopolis.university/index.php/BSc:Logic_and_Discrete_Mathematics.f22 CSE113 — Philosophy I - (Discrete Math and Logic)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I.f22 CSE201 — Mathematical Analysis I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I.f22 CSE202 — Analytical Geometry and Linear Algebra I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II.s23 CSE203 — Mathematical Analysis II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II.s23 CSE204 — Analytic Geometry And Linear Algebra II]
* [https://eduwiki.innopolis.university/index.php/BSc:_Differential_Equations.f22 CSE205 — Differential Equations]
* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics.f22 CSE206 — Probability And Statistics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_to_Optimization.F22 CSE??? — Introduction to Optimization]

=== AI ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Artificial_Intelligence CSE301 — Introduction to Artificial Intelligence]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Machine_Learning CSE302 — Introduction to Machine Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Big_Data CSE303 — Introduction to Big Data]
* [https://eduwiki.innopolis.university/index.php/BSc:_Game_Theory CSE304 — Game Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Mining CSE305 — Data Mining]
* [https://eduwiki.innopolis.university/index.php/BSc:_Information_Retrieval CSE306 — Information Retrieval]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Computer_Vision CSE307 — Introduction to Computer Vision]
* [https://eduwiki.innopolis.university/index.php/BSc:_Practical_Machine_Learning_Deep_Learning CSE308 — Practical Machine Learning And Deep Learning]
* [https://eduwiki.innopolis.university/index.php/BSc:_Statistical_Techniques_For_Data_Science CSE310 — Statistical Techniques for Data Science and Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Natural_Language_Processing CSE311 — Natural Language Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Nature_Inspired_Computing CSE340 — Nature Inspired Computing]
----

=== Hardware and Robotics ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Computer_Architecture CSE401 — Fundamentals of Computer Architecture]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_I CSE402 — Physics I (Mechanics)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Control_Theory CSE403 — Control Theory]
* [https://eduwiki.innopolis.university/index.php/BSc:_Sensors_And_Sensing CSE404 — Sensors and Sensing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechatronics CSE405 — Mechatronics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Robotics CSE406 — Fundamentals of Robotics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Mechanics_And_Machines CSE407 — Mechanics And Machines]
* [https://eduwiki.innopolis.university/index.php/BSc:_Theoretical_Mechanics CSE408 — Theoretical Mechanics]
* [https://eduwiki.innopolis.university/index.php/BSc:_Robotic_Systems CSE409 — Robotic Systems]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_II CSE410 — Physics II - Electrical Engineering]

----

=== Security and Networks ===

* [https://eduwiki.innopolis.university/index.php/BSc:_Networks CSE501 — Networks]
* [https://eduwiki.innopolis.university/index.php/BSc:_System_And_Network_Administration CSE502 — System And Network Administration]
* [https://eduwiki.innopolis.university/index.php/BSc:_Network_And_Cyber_Security CSE503 — Network And Cyber Security]
* [https://eduwiki.innopolis.university/index.php/BSc:_Signals_And_Systems CSE504 — Digital Signal Processing]
* [https://eduwiki.innopolis.university/index.php/BSc:_Dev_Ops_Engineering CSE508 — DevOps Engineering]
* [https://eduwiki.innopolis.university/index.php/BSc:_Fundamentals_of_Computer_Security CSE509 — Fundamentals of Information Security]

=== SE ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Lean_Software_Development CSE803 — Lean Software Development]
* [https://eduwiki.innopolis.university/index.php/BSc:SoftwareQualityandReliability.S22 CSE806 — Software Quality and Reliability]
* [https://eduwiki.innopolis.university/index.php/Bc:_It_Product_Development CSE807 — IT Product Development]

== Humanities ==

=== Pedagogy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_History HSS601 — History (History of Russia, World History)]
* [https://eduwiki.innopolis.university/index.php/BSc:_Physical_Culture_and_Sport HSS105 — Physical Culture and Sport]

=== Languages and literature ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture HSS203 — Foreign Language I]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS204 — Foreign Language II]

=== The science ===
* [https://eduwiki.innopolis.university/index.php/BSc:AcademicResearchandWritingCulture HSS501 — Academic Research and Writing Culture 1]
* [https://eduwiki.innopolis.university/index.php/BSc:_Academic_Research_and_Writing_Culture_II HSS502 — Academic Research and Writing Culture 2]

=== History and philosophy ===
* [https://eduwiki.innopolis.university/index.php/BSc:_Philosophy_II HSS602 — Philosophy II (Languages and Perceptions)]

----

MSc: Requirements Engineering

2022-04-22T13:34:26Z

R.akhmetzyanov: /* Prerequisites */

= Requirements Engineering =

* '''Course name:''' Requirements Engineering
* '''Course number:''' SE-

== Course characteristics ==

=== Key concepts of the class ===

* Requirements elicitation
* Requirements specification
* Requirements prototyping and implementation
* Requirements verification
* Requirements traceability

=== What is the purpose of this course? ===

The course has the following key objectives:

* To introduce the motivation, conceptual background and terminology on which requirements engineering relies.
* To provide a comprehensive account of state-of-the-art techniques for requirements engineering.
* To let the students experience the actual requirements-caused problems faced by real software teams.

== Prerequisites ==

The course has been designed to be self-included as much as possible. It can be useful to have a general understanding of the following:
* Basics of Software Development
* Basics of Software Testing
* Basics of Software design and Unified Modelling Language
* Basics of Software Development process
* Basics of Software Engineering

== Recommendations for students on how to succeed in the course ==
I suggest here some simple video material that can help a smooth introduction with the course:
* [https://www.youtube.com/watch?v=pquPUX1EihM What is Software Development]
* [https://www.youtube.com/watch?v=gNmrGZSGK1k What Are The Steps of the Software Development Lifecycle?]
* [https://www.youtube.com/watch?v=i-QyW8D3ei0 Software Development Lifecycle in 9 minutes!]
* [https://www.youtube.com/watch?v=oLc9gVM8FBM Software Testing Explained: How QA is Done Today]
* [https://www.youtube.com/watch?v=zCX-N1H8Vps Functional and Nonfunctional Requirements - Georgia Tech - Software Development Process]
* [https://www.youtube.com/watch?v=Z9QbYZh1YXY What is Agile?]
* [https://www.youtube.com/watch?v=Q0A35ZfgwHA What Professional Software Engineers ACTUALLY Do]

It can be an advantage to read introductory chapters of the main textbook:
Axel van Lamsweerde [https://www.wiley.com/en-us/Requirements+Engineering%3A+From+System+Goals+to+UML+Models+to+Software+Specifications-p-9780470012703 Requirements Engineering], Wiley Publishing (2009)

=== Course Objectives Based on Bloom’s Taxonomy ===

The “Requirements Engineering” course develops students’ skills at all the 6 levels of the Bloom’s taxonomy.

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to recognize and define:

* System requirements
* Software requirements
* Domain knowledge
* Environment assumptions
* Environment-controlled phenomena
* Machine-controlled phenomena
* Environment-observed phenomena
* Machine-observed phenomena
* Problem space
* Solution space
* Prescriptive statements
* Descriptive statements
* Traceability links

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to describe and explain (with examples):

* Difference between system and software requirements
* Difference between domain knowledge and environment assumptions
* Pairwise difference between environment- and machine-controlled (observed) phenomena
* Difference between the world and the machine
* Difference between problem and solution space
* Difference between prescriptive and descriptive statements
* Difference between vertical and horizontal traceability

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to apply:

* Requirements elicitation techniques
* Requirements specification techniques
* Prototyping and implementation techniques
* Negotiation techniques for modifying requirements
* Techniques for establishing traceability links, both vertical and horizontal
* Parameterized unit testing
* Acceptance testing

=== - What inference can a student make based on the acquired knowledge? ===

By the end of the course, the students should be able to identify:

* Lack of traceability links
* Incorrectly implemented requirements
* Incorrectly elicited requirements
* Incompletely implemented requirements
* Incompletely elicited requirements

=== - What judgements can a student make about the studied field? ===

By the end of the course, the students should be able to judge:

* Completeness of a requirements document specified by others
* Correctness of a requirements document specified by others
* Completeness of an implementation developed by others wrt requirements
* Correctness of an implementation developed by others wrt requirements
* Traceability for software artifacts created by others
* Presentations of other students

=== - What actions can students take based on their judgement? ===

By the end of the course, the students should be able to take appropriate actions for:

* Eliciting lacking requirements from stakeholders
* Negotiating requirements modifications with stakeholders
* Implementing lacking functionality, wrt to requirements, in software developed by others
* Fixing functionality that incorrectly implements requirements in software developed by others
* Introducing missing traceability links
* Writing additional tests to achieve sufficient requirements coverage

=== Course evaluation ===

== Evaluation ==

{|
|+ Course grade breakdown
| Practical assignments
| 60
|-
| Reading assignments
| 18
|-
| Project presentations
| 12
|-
| Classroom participation
| 10
|}

If necessary, please indicate freely your course’s features in terms of students’ performance assessment: None

=== Grades range ===

<div id="tab:ModelsCourseGradingRange">

{|
|+ Course grading range
!
!
!align="center"| '''Proposed range'''
|-
| A. Excellent
| 90-100
|align="center"| 80-100
|-
| B. Good
| 75-89
|align="center"| 65-79
|-
| C. Satisfactory
| 60-74
|align="center"| 50-64
|-
| D. Poor
| 0-59
|align="center"| 0-49
|}

</div>
If necessary, please indicate freely your course’s grading features: The semester starts with the default range as proposed in the Table [[#tab:ModelsCourseGradingRange|1]], but it may change slightly (usually reduced) depending on how the semester progresses.

=== Resources and reference material ===

* Handouts supplied by the instructor
*
*
*

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{|
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Requirements elicitation and documentation
|align="center"| 20
|-
|align="center"| 2
| Requirements prototyping and implementation
|align="center"| 20
|-
|align="center"| 3
| Requirements verification and traceability
|align="center"| 20
|}

=== Section 1 ===

=== Section title: ===

Requirements elicitation and documentation

=== Topics covered in this section: ===

* Foundations of requirements engineering
* The world and the machine
* Domain understanding and requirements elicitation
* Questions for interviews
* The requirements process
* Use cases
* Requirements specification and documentation

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 1 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# What is the WHY-Dimension of requirements engineering?
# What criteria are recommended to use for stakeholders analysis?
# Who is a stakeholder?
# What is an artifact-driven elicitation technique?
# What are the four principles for description in requirements engineering?
# What are the four facets of relationship between the world and the machine?
# What are the four kinds of denial in software engineering?
# What is a descriptive statement?
# What are the different kinds of information about the world?

=== Typical questions for seminar classes (labs) within this section ===

# Write down and present a project proposal for implementing during the course.
# Propose a set of questions for a requirements elicitation interview.
# Conduct, audio record and transcribe an elicitation interview.
# Design use cases based on the elicitation transcript and audio recording.

=== Test questions for final assessment in this section ===

# Present you experience of preparing and conducting the elicitation interview.
# How did you choose the stakeholder for interviewing?
# Did the interview go according to the plan?
# Which of the initially prepared questions you did not ask during the interview? Why?
# What questions you had to ask in addition to the initially prepared ones? Why?
# If you have been interviewed, how relevant were the interviewer’s questions?
# What conflicts did you have when merging the interview transcripts of your team members?
# How did you solve the merging conflicts?
# What lessons have you learned based on your experience as an interviewer and an interviewee?
# Present use cases constructed based on the elicited information.
# How do the use cases trace to the interview transcript?
# How does the interview transcript trace to the use cases?

=== Section 2 ===

=== Section title: ===

Requirements prototyping and implementation

=== Topics covered in this section: ===

* Mapping use cases to object models
* From use cases to user interface design
* Activity diagrams
* The psychopathology of everyday things
* Seamless requirements
* The anatomy of requirements

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 1 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 1 
Essays & 0 
Oral polls & 1 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# What value do UML diagrams bring to the requirements engineering process?
# Define the “extend” relationship between use cases.
# Enumerate risk reduction tactics.
# Describe defining characteristics of what Davis calls “knowledge structure”.
# What activities does the risk management process involve?
# How do you call an active component in a use case diagram?
# What is the purpose of postconditions in use cases?
# Name different types of relationships in use case modelling.
# Categorize UML as either informal, semi-formal or formal notation.
# Define the “generalization” relationship in UML.

=== Typical questions for seminar classes (labs) within this section ===

# Reflect, individually and in teams, on the use cases and the use case diagram that you received for implementation from another team.
# Construct activity diagrams from the use cases and the use case diagram.
# Construct classes based on the activity diagrams.
# Design user interfaces based on the classes and activity diagrams.
# Develop a minimum viable product (MVP) implementing your input requirements.

=== Test questions for final assessment in this section ===

# Record and present a short demo of your MVP.
# What decisions did you have to take when implementing the MVP?
# How did you define your MVP?
# What did you have to change in the requirements document, and why?
# What requirements you decided to cover and not to cover in the MVP, and why?
# Present lessons learned from developing the MVP.

=== Section 3 ===

=== Section title: ===

Requirements verification and traceability

=== Topics covered in this section: ===

* Parameterized unit tests
* Goal modelling
* Scrum & User stories
* Use case testing

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 1 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 1 
Essays & 1 
Oral polls & 1 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# How do we call an active system component playing a specific role in goal satisfaction?
# How do we call an autonomous and passive object in the object model, which cannot control the behaviours of instances of other objects?
# What is a goal?
# What goal pattern refers to every future state?
# How do we call an association where the composite object and its components appear and disappear together in the system?
# Describe the goal refinement process.
# What is object specialization?
# Define the “maintain” goal pattern.
# Enumerate and describe different relationships between goals.

=== Typical questions for seminar classes (labs) within this section ===

# Construct parameterized unit tests for the MVP provided to you by another team.
# Reflect, individually and in teams, on the MVP provided to you by another team.
# Reflect, individually and in teams, on the user interface design of the MVP.
# Develop the MVP into a usable production-quality software.
# Construct use case tests and parameterized units tests for the final implementation; update the requirements document as you go.
# Run the tests and fix the identified defects; update the requirements document as you go.
# Ensure pairwise mutual completeness between the requirements, final implementation and tests.
# Ensure pairwise mutual traceability between the requirements, final implementation and tests.
# Write a document that will describe very clearly how to run and use the final implementation.
# Describe in the document how to reproduce different use cases in the actual software.

=== Test questions for final assessment in this section ===

# Present the final system developed from the MVP received from another team.
# Introduce the project and its business goals.
# Evaluate the quality of the interview transcript.
# Evaluate the quality of the use cases.
# Evaluate the quality of the MVP and user interfaces.
# Reflect on the quality management process.
# Record and present demo of test runs.
# Reflect on teamwork and communication with other teams.
# Present lessons learned while implementing different parts of different projects coming from the other teams.
# Record and present demo runs of the software using the use cases as the reference.
# Describe strengths and weaknesses of the final implementation.
# Write an essay detailing your reflections on the overall course experience.

MSc: Offensive Technologies

2022-04-22T13:33:48Z

R.akhmetzyanov: /* Prerequisites */

= Offensive Technologies =

* Course name: Offensive Technology
* Course number: ?

== Course characteristics ==

=== Key concepts of the class ===

* Software Security
* Malware Analysis
* Mobile Security
* Network and Web Security

=== What is the purpose of this course? ===

Offensive Technology introduces methods, tools, and techniques to the students to assess the security of different services, protocols, and applications. The course aims to expose the students to real-world expertise from a security perspective and let them find vulnerabilities in both software and hardware, Also in this course, the students will learn how to analyze a malicious application and how they can understand the behavior of this application and deploy the appropriate defenses against this application. Furthermore, the students will develop projects of their choice to show their skills. In this course, the students will particularly focus on Software Testing, Fuzzing, Malware Analysis, Mobile Security, and Network and Web Security.

== Prerequisites ==
* The course has been designed to be self-included as much as possible. The successful completion will depend on prerequisite courses such as:
* [https://eduwiki.innopolis.university/index.php/MSc:Advanced_Security CSE522 - Advanced Security]
* Essential skills
* Classical Internet Applications

=== Course Objectives Based on Bloom’s Taxonomy ===

==== - What should a student remember at the end of the course? ====

By the end of the course, the students should be able to recognize and define

* Common weaknesses/vulnerabilities in web application
* ASLR, NX, and how are these techniques can help to protect against a malicious attacker
* Fuzzing techniques
* Malware C&C server
* Process injection techniques that are used in malware and how to defend against it
* Mobile security analysis

==== - What should a student be able to understand at the end of the course? ====

By the end of the course, the students should be able to describe and explain (with examples)

* Methods and techniques bypass memory mitigation techniques
* Methods and techniques for fuzz testing
* Methods and techniques malware analysis
* Methods and techniques for mobile security testing
* Methods and techniques web penetration testing

==== - What should a student be able to apply at the end of the course? ====

* Perform a network discovery
* Detect/exploit common weaknesses/vulnerabilities in web applications.
* Detect vulnerabilities in software.
* Writing an exploit to bypass ASLR and NX protection.
* Perform fuzzing for a specific use case.
* Perform security assessment for mobile application
* Perform security analysis for a malicious application

=== Course evaluation ===

{| class="wikitable" style="text-align:center"
|+ Course grade breakdown
! Type
! Default points
!align="center"| '''Proposed points'''
|-
| Labs/seminar classes
| ?
|align="center"| 50
|-
| Project
| ?
|align="center"| 50
|}

If necessary, please indicate freely your course’s features in terms of students’ performance assessment: The laboratory assessments are particularly taken care of, and the tasks do correspond with the teachings from the lectures.

=== Grades range ===

{| class="wikitable" style="text-align:center"
|+ Course grading range
! Grade
! Default range
!align="center"| Proposed range
|-
| A. Excellent
| 90-100
|align="center"| 85-100
|-
| B. Good
| 75-89
|align="center"| 70-84
|-
| C. Satisfactory
| 60-74
|align="center"| 60-69
|-
| D. Poor
| 0-59
|align="center"| 0-59
|}

If necessary, please indicate freely your course’s grading features: The laboratory assignments are mandatory with a required minimum result of 6/10 - including re-takes - to complete the course. As a consequence, the grades are generally pretty high and therefore the grading ranges are scaled up.

=== Resources and reference material ===

* Mike O’Leary, Cyber Operations, Second Edition, Apress, 2019
* Ric Messier, Penetration Testing Basics: A Quick-Start Guide to Breaking into Systems, Apress, 2016
* Michal Zalewsk, The Tangled Web, No Starch Press, 2011
* Jon Erickson, Hacking: The Art of Exploitation, 2nd Edition, No StarchPress, 2008
* The Fuzzing Bookhttps://www.fuzzingbook.org
* Wil Allsopp, Advanced Penetration Testing: Hacking the World’s most secure Networks, Wiley, 2017
* Dafydd Stuttard, The Web Application Hacker’s Handbook: Finding and exploiting Security Flaws, 2nd edition, Wiley, 2011
* Rafay Baloch, Ethical Hacking and Penetration Testing Guide, AuerbachPublications, 2014

== Course Sections ==

{| class="wikitable" style="text-align:center"
|+ Course Sections
! Section
! Section Title
!align="center"| Teaching Hours
|-
| 1
| Physical & Software Security
|align="center"| 12
|-
| 2
| Malware Analysis
|align="center"| 4
|-
| 3
| Mobile Security
|align="center"| 4
|-
| 4
| Network and Web Security
|align="center"| 4
|-
| 5
| Labs
|align="center"| 40
|-
| 6
| Project
|align="center"| 24
|}

=== Section 1 ===

'''Section title: Software Security'''

'''Topics covered in this section:'''

* Buffer overflow vulnerability
* Format string vulnerability
* ASLR defensive technique
* NX defensive technique
* Fuzzing security testing

'''What forms of evaluation were used to test students’ performance in this section?'''

{| class="wikitable" style="text-align:center"
! Form
! Yes/No
|-
| Development of individual parts of software product code
| 0
|-
| Homework and group projects
| 1
|-
| Midterm evaluation
| 0
|-
| Testing (written or computer based)
| 1
|-
| Reports
| 1
|-
| Essays
| 0
|-
| Oral polls
| 0
|-
| Discussions
| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What are the pros and cons of using ASLR? does it affect the performance?
* What is the required information to be able to identify a remote libc version?
* What are the pros and cons of writing your own fuzzer?

'''Typical questions for seminar classes (labs) within this section'''

* Write an exploit for a given binary, also try to bypass the mitigation techniques
* Implement a fuzzer for a specific use-case

'''Test questions for final assessment in this section'''

As above

=== Section 2 ===

'''Section title: Malware Analysis'''

'''Topics covered in this section:'''

* Malware evasion techniques
* Malware injection techniques
* Malware artifacts
* Virtual Machine environment hardening
* Professional malware analysis frameworks and tools

'''What forms of evaluation were used to test students’ performance in this section?'''

{| class="wikitable" style="text-align:center"
! Form
! Yes/No
|-
| Development of individual parts of software product code
| 0
|-
| Homework and group projects
| 1
|-
| Midterm evaluation
| 0
|-
| Testing (written or computer based)
| 1
|-
| Reports
| 1
|-
| Essays
| 0
|-
| Oral polls
| 0
|-
| Discussions
| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* For a given malicious application try to find useful artifacts, for example, find encryption key, C&C server, find commands that server can send
* while setup an isolated analytic Virtual Machine, What are the required steps for hardening?
* what are the most commonly used evasion and injection in malware and how can you detect it?

'''Typical questions for seminar classes (labs) within this section'''

* For a given malicious application try to find the evasion and injection techniques that are used by that application
* For a given malicious application try to write detection rules to be able to defend against it
* Setup an isolated analytic Virtual Machine and test it against Virtual Machine detection tools

'''Test questions for final assessment in this section'''

As above

=== Section 3 ===

'''Section title: Mobile Security'''

'''Topics covered in this section:'''

* Mobile architecture
* Mobile security testing
* Detection of mobile malware
* Professional mobile security testing frameworks and tools

'''What forms of evaluation were used to test students’ performance in this section?'''

{| class="wikitable" style="text-align:center"
! Form
! Yes/No
|-
| Development of individual parts of software product code
| 0
|-
| Homework and group projects
| 1
|-
| Midterm evaluation
| 0
|-
| Testing (written or computer based)
| 1
|-
| Reports
| 1
|-
| Essays
| 0
|-
| Oral polls
| 0
|-
| Discussions
| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What privilege does root or jailbreak gives you? is it mandatory for security testing?
* What is the difference from the security perspective between some version of an old mobile operation system
* what is the pros and cons of mobile security testing?

'''Typical questions for seminar classes (labs) within this section'''

* For a given malicious application try to find the evasion and injection techniques that are used by that application
* Setup an automated mobile security testing solution and test a given application
* try to bypass some of the security mechanisms that are enabled either on the application or on the operating system level

'''Test questions for final assessment in this section'''

As above

=== Section 4 ===

'''Section title: Network and Web Security'''

'''Topics covered in this section:'''

* Injection Flows
* Cookies Flows
* Server Misconfiguration
* Network Misconfiguration

'''What forms of evaluation were used to test students’ performance in this section?'''

{| class="wikitable" style="text-align:center"
! Form
! Yes/No
|-
| Development of individual parts of software product code
| 0
|-
| Homework and group projects
| 1
|-
| Midterm evaluation
| 0
|-
| Testing (written or computer based)
| 1
|-
| Reports
| 1
|-
| Essays
| 0
|-
| Oral polls
| 0
|-
| Discussions
| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What is the difference between boolean-based and time-based SQL injection?
* Can regex matching protect against Directory Traversal attack?
* Does the Same Origin Policy apply to the localStorage inside the browser?

'''Typical questions for seminar classes (labs) within this section'''

* Vulnerability analysis and exploitation for a given web application
* Write and deploy WAF rules to mitigate a specific web attack

'''Test questions for final assessment in this section'''

As above

MSc: Cybercrime Forensics

2022-04-22T13:33:04Z

R.akhmetzyanov: /* Prerequisites */

= Cybercrime and Forensics =

* Course name: Cybercrime and Forensics
* Course number: SNE-???

== Course characteristics ==

== Key concepts of the class ==

* Law, regulations and modern tendencies of the high-tech crimes
* Computer forensics approaches and techniques
* Incident response and threat hunting methods

== What is the purpose of this course? ==

Modern tactics and techniques of high-tech crimes, including counter -forensics methods, are evolving rapidly according to the past several years. Therefore, the purpose of this course is to provide for students the necessary knowledge and abilities to obtain and analyze digital evidence in a way to provide investigations that will comply with the current law and regulations. Another purpose for the course is to learn for students how to counteract with ongoing computer incidents, intrusions and to perform threat hunting in the computer systems

== Prerequisites ==
The course has been designed to be self-included as much as possible. The successful completion will depend on prerequisite courses such as:
* [https://eduwiki.innopolis.university/index.php/MSc:_Security_of_systems_and_networks CSE520 — Security of systems and networks]
* Essential skills
* Classical Internet Applications

== Course Objectives Based on Bloom’s Taxonomy ==

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to identify and define

* Methods for investigating and responding to cybersecurity incidents
* Main types of computer attacks and the technical and non-technical techniques used by attackers;
* Compliance requirements to produce valid computer-technical expertise for further legal procedures;
* Aquisition techniques depending on the affected digital media and environment conditions
* Computer systems’ artifacts that were affected during the incident
* Specific hardware and software forensic tools depending on the type of incident;
* Decryption and decoding methods for protected and hidden data, methods of counter-forensics technology.

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to describe and explain

* Difference between different types of computer incidents
* The difference in compliance requirements for specific cybercrime cases
* Computer attacker model and kill chain tactics
* Filesystems analysis methods
* Volatile memory analysis methods
* Network analysis methods
* Malware analysis methods

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to demonstrate

* Organizing an incident response to a cybersecurity incident and minimize potential damage
* Determination of the type and causes of the incident
* Determination of the computer systems’ artifacts that are required for the acquisition
* Collection of digital evidence and proper documentation of it
* Recovered deleted and hidden information
* Restored an incident chronology during the investigation, determination of the methods used by the attacker and the impact on the attacked system
* Conduction of investigation on various types of computer attacks
* Conduction malware analysis
* Correct and efficient usage of open source forensics software and hardware

== Course evaluation ==

{|
|+ Course grade breakdown
!align="center"| '''Type'''
!align="center"| '''Default points'''
!align="center"| '''Proposed points'''
|-
|align="center"| Labs/seminar classes
|align="center"| 20
|align="center"| 20
|-
|align="center"| Project
|align="center"| 30
|align="center"| 60
|-
|align="center"| Exam
|align="center"| 50
|align="center"| 20
|}

If necessary, please indicate freely your course’s features in terms of students’ performance assessment: None

== Grades range ==

{|
|+ Course grading range
!align="center"| '''Grade'''
!align="center"| '''Default range'''
!align="center"| '''Proposed range'''
|-
|align="center"| A. Excellent
|align="center"| 90-100
|align="center"| 90-100
|-
|align="center"| B. Good
|align="center"| 75-89
|align="center"| 70-89
|-
|align="center"| C. Satisfactory
|align="center"| 60-74
|align="center"| 60-69
|-
|align="center"| D. Poor
|align="center"| 0-59
|align="center"| 0-59
|}

If necessary, please indicate freely your course’s grading features:

The laboratory assignments are mandatory with a required minimum result of 6/10 for each - including re-takes and late submissions - to complete the course. The semester starts with the default range as proposed in the Table above, but it may changes slightly depending on how the semester progresses.

== Resources and reference material ==

* “Practical forensic imaging. Securing digital evidence with Linux tools”. Bruce Nikkel
* “Incident response and computer forensics”. K.Mandia, C.Prosise, and M.Pepe
* “Digital Evidence and Computer Crime”. Eoghan Casey

== Course Sections ==

{|
|+ Course Sections
!align="center"| '''Section'''
!align="center"| '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
|align="center"| Modern high-tech crimes and the law
|align="center"| 4
|-
|align="center"| 2
|align="center"| Data acquisition and securing digital evidence
|align="center"| 4
|-
|align="center"| 3
|align="center"| Computer systems’ artifacts and their analysis methods
|align="center"| 6
|-
|align="center"| 4
|align="center"| Volatile data analysis methods
|align="center"| 6
|-
|align="center"| 5
|align="center"| Incident response and threat hunting
|align="center"| 4
|-
|align="center"| 6
|align="center"| Labs
|align="center"| 56
|}

== Section 1 ==

'''Section title:''' Modern high-tech crimes and the law

'''Topics covered in this section:'''

* Law, regulations and modern tendencies of the high-tech crimes
* Computer forensics approaches and techniques
* Incident response and threat hunting methods

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What are the typical attacks which can be used against the banking system?
* What is the attacker model?
* What is the computer incident?
* What types of incidents can lead to criminal code articles for an attacker?

'''Typical questions for seminar classes (labs) within this section'''

* Identify risks and develop mitigation techniques before acquiring evidence for a given case
* Develop an attacker model for a specific incident
* Identify the most important compliance requirements for preservation evidence in the court case?

'''Test questions for final assessment in this section'''

* As above

== Section 2 ==

'''Section title:''' Data acquisition and securing digital evidence

'''Topics covered in this section:'''

* Compliance requirements for the evidence acquisition
* Non-volatile data evidence collection
* Volatile data evidence collection
* Securing digital evidence with open source tools

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What are the pros and cons of using software or hardware tools for acquisition?
* What are the important steps to perform data evidence acquisition on the live system?
* What is the difference between non-volatile and volatile data from the perspective of computer forensics?
* What are the legal aspects of preparing before conducting computer forensic analysis based on the positions and responsibilities of forensic investigators?
* What kind of computer systems’ components would be less important during a live acquisition?

'''Typical questions for seminar classes (labs) within this section'''

* Depending on the incident define software and hardware that can be used to collect and preserve digital evidence
* Collect the evidence on a virtual environment
* Collect the evidence from the live system
* Collect the evidence of the volatile data
* Provide integrity, confidentiality, and non-repudiation for acquired evidence

'''Test questions for final assessment in this section'''

As above

== Section 3 ==

'''Section title:'''

'''Topics covered in this section:''' Computer systems’ artifacts and their analysis methods

* Anti-forensics methods and recovery information
* Windows forensics
* Filesystem forensics

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What kind of methods do you know for an attacker to hide and delete information?
* What are the important artifacts that can be used for the analysis of the Windows systems?
* What is the difference between DEFT and CAIN software forensics distributions?
* What is MAC time?
* What is the conceptual difference between FAT and NTFS?

'''Typical questions for seminar classes (labs) within this section'''

* Analyze the incident that involves the USB stick of the attacker
* Create a timeline based on the timestamps of the artifacts
* Find and recover hidden information on the hard drive
* Extract and analyze filesystem journals
* Find encrypted information
* Identify the slack spaces that contain deleted data

'''Test questions for final assessment in this section'''

As above

== Section 4 ==

'''Section title:''' Volatile data analysis methods

'''Topics covered in this section:'''

* Operating memory forensics
* Network forensics

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What is fileless malware?
* How can rootkits affect the evidence?
* What kind of operating memory artifacts can be useful for cybercrime investigation?
* What is difficult about dumping a memory?
* What is difficult about dumping network traffic?

'''Typical questions for seminar classes (labs) within this section'''

* Identify direct kernel object manipulation in the given sample
* Find unlinking from the active process list
* Trace and detect used cryptographical keys on the incident
* Determine the original source of an attacker’s compromise on the given network traffic
* Establish and present a timeline of the attacker’s activities for a specific case

'''Test questions for final assessment in this section'''

As above

== Section 5 ==

'''Section title:''' Incident response and threat hunting

'''Topics covered in this section:'''

* Incident response techniques
* Sandboxing
* Malware analysis
* Threats hunting

'''What forms of the evaluation were used to test students’ performance in this section?'''

{|
!align="center"| '''Form'''
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

'''Typical questions for ongoing performance evaluation within this section'''

* What limitations might you have during the incident response?
* What type of incident responses can be provided during the incident
* What is the difference between incident response and computer forensics in general
* What is sandboxing and how it could be used in the incident response?
* What type of threats can occur for investigators during investigation?

'''Typical questions for seminar classes (labs) within this section'''

* Identify the methods that can detect anomaly behavior for a typical Windows system processes
* Identify persistence mechanisms that are used by the given malicious process
* Identify illegitimate network activity on the given network traffic
* Develop an effective sandboxing environment for malware detection and examination of its behavior
* Develop indicators of compromise to detect threats on multiple systems

'''Test questions for final assessment in this section'''

As above

MSc: Syllabi Index

2022-04-22T13:32:35Z

R.akhmetzyanov: /* Security and Networks */

= Numbered courses =

== Data Science ==

* CSE132 — Software Design with Python
* [https://eduwiki.innopolis.university/index.php/MSc:_Machine_Learning CSE328 — Machine Learning]
* [https://eduwiki.innopolis.university/index.php/MSc:_Empirical_Methods CSE329 — Empirical Methods]
* [https://eduwiki.innopolis.university/index.php/MSc:_Big_Data_Technologies_And_Analytics CSE330 — Big Data Technologies And Analytics]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Statistics CSE331 — Advanced Statistics]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Machine_Learning CSE332 — Advanced Machine Learning]
* [https://eduwiki.innopolis.university/index.php/MSc:_Optimization CSE333 — Optimization]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Information_Retrieval CSE334 — Advanced Information Retrieval]
* [https://eduwiki.innopolis.university/index.php/MSc:_High-Dimensional_Data_Analysis CSE335 — High-Dimensional Data Analysis]
* [https://eduwiki.innopolis.university/index.php/MSc:_Computer_Vision CSE427 — Computer Vision]

== Robotics ==
* [https://eduwiki.innopolis.university/index.php/MSc:_Fundamentals_of_Robot_Control CSE424 — Fundamental of Robot Control]
* [https://eduwiki.innopolis.university/index.php/MSc:_Sensing_Perception_Actuation CSE425 — Sensing, Perception And Actuation]
* [https://eduwiki.innopolis.university/index.php/MSc:_Dynamics_Of_Non_Linear_Robotic_Systems CSE426 — Dynamics Of Non-Linear Robotic Systems]
* [https://eduwiki.innopolis.university/index.php/MSc:_Computational_Intelligence CSE428 — Computational Intelligence]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Robotics CSE429 — Advanced Robotics]
* [https://eduwiki.innopolis.university/index.php/MSc:_Large_Systems CSE521 — Large Systems]

== Security and Networks ==
* [https://eduwiki.innopolis.university/index.php/MSc:_Classical_Internet_Applications CSE517 — Classical Internet Applications]
* [https://eduwiki.innopolis.university/index.php/MSc:_Inter_Networking_and_Routing CSE518 — Internetworking And Routing]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Networking CSE519 — Advanced Networking]
* [https://eduwiki.innopolis.university/index.php/MSc:_Security_of_systems_and_networks CSE520 — Security of systems and networks]
* [https://eduwiki.innopolis.university/index.php/MSc:_Advanced_Security CSE522 — Advanced Security]
* [https://eduwiki.innopolis.university/index.php/MSc:_Cybercrime_Forensics CSE523 — Cybercrime And Forensics]
* [https://eduwiki.innopolis.university/index.php/MSc:_Offensive_Technologies CSE524 — Offensive Technologies]

== Software Engineering ==
* [https://eduwiki.innopolis.university/index.php/MSc:_Managing_Software_Development CSE820 — Managing Software Development]
* [https://eduwiki.innopolis.university/index.php/MSc:_Requirements_Engineering CSE821 — Requirements Engineering]
* [https://eduwiki.innopolis.university/index.php/MSc:_Personal_Software_Process CSE822 — Personal Software Process]
* [https://eduwiki.innopolis.university/index.php/MSc:_Models_Software_Systems CSE823 — Models of Software Systems]
* [https://eduwiki.innopolis.university/index.php/MSc:_Architectures_of_Software_Systems CSE824 — Architectures of Software Systems]
* [https://eduwiki.innopolis.university/index.php/MSc:_Analysis_Software_Artifacts CSE825 — Analysis of Software Artifacts]
* [https://eduwiki.innopolis.university/index.php/MSc:_Communication CSE826 — Communication]

== Projects ==
*[https://eduwiki.innopolis.university/index.php/MSc:_Research_Project_SNE CSE525 — Research Project (SNE)]
*[https://eduwiki.innopolis.university/index.php/MSc:_Industrial_SNE_Project CSE526 — Industrial SNE Project]
*[https://eduwiki.innopolis.university/index.php/MSc:_Industrial_Project CSE533 — Industrial Project (MIST-SE)]
*[https://eduwiki.innopolis.university/index.php/MSc:_Master_Practicum_Project CSE602 — Master Practicum Project]
*[https://eduwiki.innopolis.university/index.php/MSc:_Project_R CSE711 — Project R]

== Humanitarian ==
*[https://eduwiki.innopolis.university/index.php/MSc:_SystematicLiteratureReviewForDataScience&Robotics HSS501 — Systematic Literature Review for Data Science & Robotics]
*[https://eduwiki.innopolis.university/index.php/MSc:_Unit-economics_For_IT_startups HSS*** — Unit-economics for IT startups: metrics-based decision making]

MSc: Classical Internet Applications

2022-04-22T13:29:52Z

R.akhmetzyanov: /* Prerequisites */

= Classical Internet Applications =

* '''Course name:''' Classical Internet Applications (CIA)
* '''Course number:''' SNE-03

== Course characteristics ==

=== Key concepts of the class ===

* Internet applications
* Focus on the application layer of the TCP/IP protocol stack
* Implementation, configuration, and security of Internet applications

=== What is the purpose of this course? ===

CIA course serves as kick-start for the security and network engineering Masters program. Before diving into the depth of the topics, the students must know preliminary concepts related to computer networks services and applications therein. This course is designed to cover the basic services offered by the Internet including operating systems and computer architecture. The concepts from this course will be used throughout the course of whole masters. More precisely, this course will cover the basic computer architecture and assembly language programming, Domain Name Services (DNS), DNSSec, email, web, directories, and disks. This course will also cover protocols and ABNF. The theory part will strengthen the theoretical aspects of the concepts whereas the lab exercises will provide the students with the opportunity to have hands-on experience of the ideas they learnt in the lectures.

== Prerequisites ==
* The course will benefit if students already know some of the following topics: Basics of TCP/IP networks: TCP/IP stack, transport protocols, application layer protocols, IP addressing, OSI -7 layer model
* Linux OS: architecture, system calls, ELF binary structure, networking and administration
* Shell Scripting: ability to work with the terminal as administrator and automatise tasks

== Recommendations for students on how to succeed in the course ==
References:
* [https://www.linuxtopia.org/online_books/linux_for_beginners_index.html Linux for Beginners]
* [https://www.linuxtopia.org/online_books/linux_kernel/kernel_configuration/index.html Linux Kernel and Architecture]
* [https://www.linuxtopia.org/online_books/linux_network_administrators_guide/index.html Linux Administration and Networking]
* [https://www.shellscript.sh/ Shell Scripting]
* [https://cds.cern.ch/record/676686/files/0130384887_TOC.pdf Computer Networks]

=== Course Objectives Based on Bloom’s Taxonomy ===

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to:

* Identify different Internet applications and understand their working principles from the protocols point of view
* Demonstrate the acquired knowledge and skills in classical internet applications including DNS, Email, and Directory services.
* Able to write regular expressions and context-free grammar that are essential in Internet applications and information exchange through the networks
* Able to partition disks and remember the booting principles as well as secure booting

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to describe and explain (with examples)

* Demonstrate knowledge and skills to use web services
* Demonstrate the essential knowledge of disks and calculate particular locations/addresses in disks
* Reason about problems in the current DNS and the need to upgrade to DNSSEC and DNS over HTTPS
* Demonstrate the knowledge of email and other services configuration

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to apply the acquired knowledge in the following way.

* Install, Configure, update, and manage DNS services over a network
* Configure, maintain, and update the secure DNS over a network
* Update, add, and delete records in DNS
* Configure a secure mail server and maintain it
* Get hands-on experience of the afore-mentioned technologies on their own servers.

=== Course evaluation ===

<div id="tab:CIACourseGrading">

{|
|+ Course grade breakdown
!
!
!align="center"| '''Proposed points'''
|-
| Labs/seminar classes
| 20
|align="center"| 20
|-
| Lab tasks assessment
| 40
|align="center"| 40
|-
| Exams
| 40
|align="center"| 40
|}

</div>
If necessary, please indicate freely your course’s features in terms of students’ performance assessment: None

=== Grades range ===

<div id="tab:CIACourseGradingRange">

{|
|+ Course grading range
!
!
!align="center"| '''Proposed range'''
|-
| A. Excellent
| 90-100
|align="center"|
|-
| B. Good
| 75-89
|align="center"|
|-
| C. Satisfactory
| 60-74
|align="center"|
|-
| D. Poor
| 0-59
|align="center"|
|}

</div>
If necessary, please indicate freely your course’s grading features: The semester starts with the default range as proposed in the Table [[#tab:CIACourseGradingRange|2]], but it may change slightly (usually reduced) depending on how the semester progresses.

=== Resources and reference material ===

* Lecture slides
* RFCs
* Link to the online material will be provided (if any)

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{|
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Intro, Booting, Operating Systems Essentials, and Computer Architecture
|align="center"| 8
|-
|align="center"| 2
| DNS, DNSSEC, DoH
|align="center"| 14
|-
|align="center"| 3
| Email
|align="center"| 4
|-
|align="center"| 4
| ABNF, Deflating, Directory Services, Disks, Web, and Protocols
|align="center"| 12
|-
|align="center"| 5
| Labs
|align="center"| 56
|}

=== Section 1 ===

=== Section title: ===

Booting, Operating Systems, and Computer Architecture

=== Topics covered in this section: ===

* Booting principles and disks
* Essentials of operating systems
* Fundamentals of computer architecture

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 1 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# What are the different steps in booting?
# How the disks are partitioned?
# Explain UEFI booting

=== Typical questions for seminar classes (labs) within this section ===

# Demonstrating the knowledge of booting
# Analyze assembly code at a basic level
# Demonstrate the system and library calls in operating systems

=== Test questions for final assessment in this section ===

# Same as above

=== Section 2 ===

=== Section title: ===

DNS, DNSSEC, and DoH

=== Topics covered in this section: ===

* DNS
* DNSSEC
* DoH

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 1 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Hows does DNS query get resolved?
# What is iterative and recursive DNS server?
# How does wildcard work in DNS?
# What is zone walking in secure DNS?
# What is delegation in DNS?
# What is NSEC and NSEC3 records in DNSSEC?
# What is the difference between DNSSEC and DoH?

=== Typical questions for seminar classes (labs) within this section ===

# Configure DNS, DNSSEC, and DoH (with specific tasks)

=== Test questions for final assessment in this section ===

# How does DNS query get resolved in DNS?
# How zones are formed?
# How delegation works in DNS?
# How resource records are verified in DNSSEC?
# What is meant by zone walking and how is it avoided?
# Why do we need DNSSEC where we can use simple DNS over HTTPS?

=== Section 3 ===

=== Section title: ===

Email

=== Topics covered in this section: ===

* Email architecture
* Spam management

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 1 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# What are different protocols used in email?
# What are the different agents used in email system?
# How to avoid spamming in email?
# How to configure email servers?

=== Typical questions for seminar classes (labs) within this section ===

# Configure email server
# Configure anti-spamming techniques

=== Test questions for final assessment in this section ===

# What are different agents and their roles in email architecture?
# How MX records work?
# How to configure different anti-spamming policies?

=== Section 4 ===

=== Section title: ===

Directory, Web, Protocol, ABNF and Deflating

=== Topics covered in this section: ===

* Directory services
* Web
* Protocols
* ABNF
* Deflating

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 1 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# How directory services are implemented?
# How permissions are set?
# How disk partitions are made?
# How different web protocols work?
# How network packets are management in a standard way through ABNF?
# How to write new formats through ABNF?

=== Typical questions for seminar classes (labs) within this section ===

# Implement different web services
# Configure active directory

=== Test questions for final assessment in this section ===

# As above

MSc: Advanced Robotics

2022-04-22T13:28:23Z

R.akhmetzyanov: /* Prerequisites */

= Advanced Robotics =

[C:AdvancedRobotics]

* '''Course name:''' Advanced Robotics
* '''Course number:'''

== Prerequisites ==
The course will benefit if students already know some topics of mathematics and programming.

Programming:
* Matlab or Python, Numpy library,
* Google Colab environment.
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I CSE201] — Mathematical Analysis I
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II CSE203] — Mathematical Analysis II: differentiation, exponentials, gradient.
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I1 CSE202] — Analytical Geometry and Linear Algebra I
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II CSE204] — Analytic Geometry And Linear Algebra II: matrix multiplication, change of the bases, orthonormal spaces, cross product and skew-symmetric matrices, eigenvector and eigenvalue, SVD.
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_I CSE402] — Physics I (Mechanics) and [https://eduwiki.innopolis.university/index.php/BSc:_Physics_II CSE410] — Physics II - Electrical Engineering]: Kinematics, Statics and Dynamics.
* Statistics: Linear regression, .Covariance matrix, Information matrix, Observability matrix, Design of Experiments, Statistical evaluation
* Screw theory.
* Product of Exponents (PoE)

== Course characteristics ==

== What subject area does your course (discipline) belong to? ==

Robotic control.

== Key concepts of the class ==

* Elastostatic modeling and calibration of robots
* Advanced control approaches for compliant robotic systems

== What is the purpose of this course? ==

While traditional robotics studies rigid robots and manipulators, many practical robotic systems exhibit non-negligible compliance. Its effects can be both detrimental (for instance, decrease in positioning accuracy of industrial manipulators) and beneficial (improved safety during human-robot interaction), depending on the application. However, regardless of whether the robot’s compliance is positive or negative, it must be accurately accounted for during modeling, trajectory tracking and robot control tasks. The main purpose of this course is to introduce elastostatic modeling of manipulators and robotic systems, methods for calibration of these devices, as well as advanced approaches to control robotic systems with non-negligible stiffness.

== Recommendations for students on how to succeed in the course ==
References:
* Any text book on Linear algebra, Calculus, Statistics, Programming and Physics
* [https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab 3blue1brown playlist on Linear Algebra] can help to overview selected topics.
* [https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/ Gilbert Strang] is one of the best human teachers of Algebra, if you prefer classic lectures to fancy videos.
* Kick start your numpy with the official [https://numpy.org/doc/stable/user/quickstart.html quickstart guide].
* Statistics for Applications

* Dimentberg, F. M. (1965) [http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=AD0680993 The Screw Calculus and Its Applications in Mechanics]

== Course Objectives Based on Bloom’s Taxonomy ==

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to remember

* How to derive expressions for position kinematics and differential kinematics of serial manipulators,
* What approaches exist to model robot joints’ elasticity,
* How to model dynamics of compliant robots,
* Fundamental principles of position tracking control for robots with compliance,
* Motivation behind energy-based approaches to control elastic robots.

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to describe and explain

* How to find Jacobian for series and parallel robots and use it to compute forces and torques,
* What constitutes a common manipulator calibration procedure,
* Reasons and examples of singularities for serial and parallel robots,
* How to drive elastic robots into limit cycles and what benefits does it bring in terms of control effort,
* How to model and control tendon-driven robots.

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to

* Find stiffness matrix for given manipulator,
* Analyze joint constraints and find singularities,
* Perform robot calibration procedure,
* Apply passivity principle to design stable position controllers,
* Design force controller for elastic and compliant robots.

== Course evaluation ==

{|
|+ Course grade breakdown
!
!
!align="center"| '''Proposed points'''
|-
| Labs/seminar classes
| 20
|align="center"| 10
|-
| Interim performance assessment
| 30
|align="center"| 60
|-
| Exams
| 50
|align="center"| 30
|}

If necessary, please indicate freely your course’s features in terms of students’ performance assessment:

The course grades are given according to the following rules: In-class discussion and lab performance = 10 pts, Homework assignments (4) = 20 pts, Quizzes (4) = 20 pts, Exams = 30 pts, Term project = 20 pts.

== Grades range ==

{|
|+ Course grading range
!
!
!align="center"| '''Proposed range'''
|-
| A. Excellent
| 90-100
|align="center"|
|-
| B. Good
| 75-89
|align="center"|
|-
| C. Satisfactory
| 60-74
|align="center"|
|-
| D. Poor
| 0-59
|align="center"|
|}

If necessary, please indicate freely your course’s grading features.

== Resources and reference material ==

Textbooks:

*
*
*
*
*
*

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{|
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Stiffness modeling
|align="center"| 6
|-
|align="center"| 2
| Robot calibration
|align="center"| 6
|-
|align="center"| 3
| Position tracking
|align="center"| 6
|-
|align="center"| 4
| Energy, impedance, and force control
|align="center"| 6
|}

=== Section 1 ===

==== Section title: ====

Stiffness modeling

=== Topics covered in this section: ===

* Position and velocity kinematics
* Virtual joint modeling
* Finite element analysis
* Matrix structural analysis

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Name types of robot workspace.
# Name key features and differences between serial and parallel manipulators.
# What is Jacobian matrix and how to use it for singularity analysis.
# What is stiffness matrix of manipulator and what does it describe?

=== Typical questions for seminar classes (labs) within this section ===

# Find stiffness matrix of a given parallel robotic platform.
# Apply direct FEA method to analyze compliance of a given manipulator.
# Perform matrix structural analysis of a cantilever beam.
# Find stiffness matrix of a two-link manipulator with elastic joint.
# Model stiffness of a non-rigid mobile platform.

=== Test questions for final assessment in this section ===

# Describe main stiffness modeling approaches, their particularities, advantages and limitations.
# Use variable joint model for a serial manipulator (assume all elements are flexible) to find stiffness matrix.
# Drive VSJ and MSA models of the tripteron robot shown.

=== Section 2 ===

==== Section title: ====

Robot calibration

=== Topics covered in this section: ===

* Types of robot calibration
* Sources of uncertainties and model errors in practical robots
* Robot errors
* Complete, irreducible geometric models
* Elastostatic calibration

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Why is robot calibration needed?
# What are the main sources of errors in robot parameters?
# Give examples of geometric and non-geometric errors.
# Describe typical steps of calibration procedure.

=== Typical questions for seminar classes (labs) within this section ===

# Drive information matrix of a 2-link manipulator.
# Estimate identification accuracy for 4-link manipulator.
# Comment on differences between compliance matrix of a manipulator obtained via CAD modeling and identification results.
# Perform model reduction for a given manipulator.

=== Test questions for final assessment in this section ===

# Describe particularities and difficulties of the elastostatic calibration.
# What do good/bad accuracy and repeatability mean?
# What is complete, irreducible geometric model and why do we need it?
# Find complete and irreducible model for geometric calibration of robot presented below.

=== Section 3 ===

==== Section title: ====

Position tracking

==== Topics covered in this section: ====

* Adaptive control of flexible joint manipulators
* Adaptive robust control
* Modeling and control of cable-driven robotic systems

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# What challenges does robot compliance pose for a control system?
# What are the mathematical fundamentals of adaptive control?
# How does cable elasticity affect dynamics of tendon-driven robots?
# How to perform feedback linearization for a given compliant robot?

==== Typical questions for seminar classes (labs) within this section ====

# Design PD controller with gravity compensation for a manipulator with elastic joint.
# Numerically model behavior of compliant robot with nonlinear controller.
# Numerically model and compare accuracy and power efficiency of robust and adaptive controllers for a cable-driven robot.
# Analyze stability of adaptive controller.

==== Test questions for final assessment in this section ====

# Provide examples of practical systems with non-collocated feedback. What unique challenges does this pose for control systems?
# Design a position tracking controller for a given compliant system.
# Analyze stability of a given nonlinear control approach.

=== Section 4 ===

==== Section title: ====

Energy, impedance, and force control

==== Topics covered in this section: ====

* Energy-based control of compliant robots
* Limit cycles
* Passivity-based control
* Impedance control

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Provide examples of passive and active systems.
# What are limit cycles?
# What components of mechanical energy exist in robots with compliance?
# What happens with the energy of passive systems with time?

==== Typical questions for seminar classes (labs) within this section ====

# Find limit cycles of a given robot with compliance.
# Design gravity and compliance compensator for a robot with flexible joints.
# Simulate numerically behavior of a compliant robot during cyclic motion.
# Implement and simulate passivity-based control over given robot.

==== Test questions for final assessment in this section ====

# What are the physical fundamentals behind the concept of passivity and passivity-based control?
# Drive the dynamics of a given elastically actuated robot.
# Analyze stability of a given system with passivity-based controller.

MSc: Advanced Robotics

2022-04-22T13:26:51Z

R.akhmetzyanov: /* Advanced Robotics */

= Advanced Robotics =

[C:AdvancedRobotics]

* '''Course name:''' Advanced Robotics
* '''Course number:'''

== Prerequisites ==
The course will benefit if students already know some topics of mathematics and programming.

Programming:
* Matlab or Python, Numpy library,
* Google Colab environment.
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I CSE201] — Mathematical Analysis I
* [CSE203] — Mathematical Analysis II: differentiation, exponentials, gradient.
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I1 CSE202] — Analytical Geometry and Linear Algebra I
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II CSE204] — Analytic Geometry And Linear Algebra II: matrix multiplication, change of the bases, orthonormal spaces, cross product and skew-symmetric matrices, eigenvector and eigenvalue, SVD.
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_I CSE402] — Physics I (Mechanics) and [https://eduwiki.innopolis.university/index.php/BSc:_Physics_II CSE410] — Physics II - Electrical Engineering]: Kinematics, Statics and Dynamics.
* Statistics: Linear regression, .Covariance matrix, Information matrix, Observability matrix, Design of Experiments, Statistical evaluation
* Screw theory.
* Product of Exponents (PoE)

== Course characteristics ==

== What subject area does your course (discipline) belong to? ==

Robotic control.

== Key concepts of the class ==

* Elastostatic modeling and calibration of robots
* Advanced control approaches for compliant robotic systems

== What is the purpose of this course? ==

While traditional robotics studies rigid robots and manipulators, many practical robotic systems exhibit non-negligible compliance. Its effects can be both detrimental (for instance, decrease in positioning accuracy of industrial manipulators) and beneficial (improved safety during human-robot interaction), depending on the application. However, regardless of whether the robot’s compliance is positive or negative, it must be accurately accounted for during modeling, trajectory tracking and robot control tasks. The main purpose of this course is to introduce elastostatic modeling of manipulators and robotic systems, methods for calibration of these devices, as well as advanced approaches to control robotic systems with non-negligible stiffness.

== Recommendations for students on how to succeed in the course ==
References:
* Any text book on Linear algebra, Calculus, Statistics, Programming and Physics
* [https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab 3blue1brown playlist on Linear Algebra] can help to overview selected topics.
* [https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/ Gilbert Strang] is one of the best human teachers of Algebra, if you prefer classic lectures to fancy videos.
* Kick start your numpy with the official [https://numpy.org/doc/stable/user/quickstart.html quickstart guide].
* Statistics for Applications

* Dimentberg, F. M. (1965) [http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=AD0680993 The Screw Calculus and Its Applications in Mechanics]

== Course Objectives Based on Bloom’s Taxonomy ==

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to remember

* How to derive expressions for position kinematics and differential kinematics of serial manipulators,
* What approaches exist to model robot joints’ elasticity,
* How to model dynamics of compliant robots,
* Fundamental principles of position tracking control for robots with compliance,
* Motivation behind energy-based approaches to control elastic robots.

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to describe and explain

* How to find Jacobian for series and parallel robots and use it to compute forces and torques,
* What constitutes a common manipulator calibration procedure,
* Reasons and examples of singularities for serial and parallel robots,
* How to drive elastic robots into limit cycles and what benefits does it bring in terms of control effort,
* How to model and control tendon-driven robots.

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to

* Find stiffness matrix for given manipulator,
* Analyze joint constraints and find singularities,
* Perform robot calibration procedure,
* Apply passivity principle to design stable position controllers,
* Design force controller for elastic and compliant robots.

== Course evaluation ==

{|
|+ Course grade breakdown
!
!
!align="center"| '''Proposed points'''
|-
| Labs/seminar classes
| 20
|align="center"| 10
|-
| Interim performance assessment
| 30
|align="center"| 60
|-
| Exams
| 50
|align="center"| 30
|}

If necessary, please indicate freely your course’s features in terms of students’ performance assessment:

The course grades are given according to the following rules: In-class discussion and lab performance = 10 pts, Homework assignments (4) = 20 pts, Quizzes (4) = 20 pts, Exams = 30 pts, Term project = 20 pts.

== Grades range ==

{|
|+ Course grading range
!
!
!align="center"| '''Proposed range'''
|-
| A. Excellent
| 90-100
|align="center"|
|-
| B. Good
| 75-89
|align="center"|
|-
| C. Satisfactory
| 60-74
|align="center"|
|-
| D. Poor
| 0-59
|align="center"|
|}

If necessary, please indicate freely your course’s grading features.

== Resources and reference material ==

Textbooks:

*
*
*
*
*
*

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{|
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Stiffness modeling
|align="center"| 6
|-
|align="center"| 2
| Robot calibration
|align="center"| 6
|-
|align="center"| 3
| Position tracking
|align="center"| 6
|-
|align="center"| 4
| Energy, impedance, and force control
|align="center"| 6
|}

=== Section 1 ===

==== Section title: ====

Stiffness modeling

=== Topics covered in this section: ===

* Position and velocity kinematics
* Virtual joint modeling
* Finite element analysis
* Matrix structural analysis

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Name types of robot workspace.
# Name key features and differences between serial and parallel manipulators.
# What is Jacobian matrix and how to use it for singularity analysis.
# What is stiffness matrix of manipulator and what does it describe?

=== Typical questions for seminar classes (labs) within this section ===

# Find stiffness matrix of a given parallel robotic platform.
# Apply direct FEA method to analyze compliance of a given manipulator.
# Perform matrix structural analysis of a cantilever beam.
# Find stiffness matrix of a two-link manipulator with elastic joint.
# Model stiffness of a non-rigid mobile platform.

=== Test questions for final assessment in this section ===

# Describe main stiffness modeling approaches, their particularities, advantages and limitations.
# Use variable joint model for a serial manipulator (assume all elements are flexible) to find stiffness matrix.
# Drive VSJ and MSA models of the tripteron robot shown.

=== Section 2 ===

==== Section title: ====

Robot calibration

=== Topics covered in this section: ===

* Types of robot calibration
* Sources of uncertainties and model errors in practical robots
* Robot errors
* Complete, irreducible geometric models
* Elastostatic calibration

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Why is robot calibration needed?
# What are the main sources of errors in robot parameters?
# Give examples of geometric and non-geometric errors.
# Describe typical steps of calibration procedure.

=== Typical questions for seminar classes (labs) within this section ===

# Drive information matrix of a 2-link manipulator.
# Estimate identification accuracy for 4-link manipulator.
# Comment on differences between compliance matrix of a manipulator obtained via CAD modeling and identification results.
# Perform model reduction for a given manipulator.

=== Test questions for final assessment in this section ===

# Describe particularities and difficulties of the elastostatic calibration.
# What do good/bad accuracy and repeatability mean?
# What is complete, irreducible geometric model and why do we need it?
# Find complete and irreducible model for geometric calibration of robot presented below.

=== Section 3 ===

==== Section title: ====

Position tracking

==== Topics covered in this section: ====

* Adaptive control of flexible joint manipulators
* Adaptive robust control
* Modeling and control of cable-driven robotic systems

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# What challenges does robot compliance pose for a control system?
# What are the mathematical fundamentals of adaptive control?
# How does cable elasticity affect dynamics of tendon-driven robots?
# How to perform feedback linearization for a given compliant robot?

==== Typical questions for seminar classes (labs) within this section ====

# Design PD controller with gravity compensation for a manipulator with elastic joint.
# Numerically model behavior of compliant robot with nonlinear controller.
# Numerically model and compare accuracy and power efficiency of robust and adaptive controllers for a cable-driven robot.
# Analyze stability of adaptive controller.

==== Test questions for final assessment in this section ====

# Provide examples of practical systems with non-collocated feedback. What unique challenges does this pose for control systems?
# Design a position tracking controller for a given compliant system.
# Analyze stability of a given nonlinear control approach.

=== Section 4 ===

==== Section title: ====

Energy, impedance, and force control

==== Topics covered in this section: ====

* Energy-based control of compliant robots
* Limit cycles
* Passivity-based control
* Impedance control

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Provide examples of passive and active systems.
# What are limit cycles?
# What components of mechanical energy exist in robots with compliance?
# What happens with the energy of passive systems with time?

==== Typical questions for seminar classes (labs) within this section ====

# Find limit cycles of a given robot with compliance.
# Design gravity and compliance compensator for a robot with flexible joints.
# Simulate numerically behavior of a compliant robot during cyclic motion.
# Implement and simulate passivity-based control over given robot.

==== Test questions for final assessment in this section ====

# What are the physical fundamentals behind the concept of passivity and passivity-based control?
# Drive the dynamics of a given elastically actuated robot.
# Analyze stability of a given system with passivity-based controller.

MSc: Computational Intelligence

2022-04-22T13:24:23Z

R.akhmetzyanov: /* Prerequisites */

= Computational intelligence =

* '''Course name:''' Computational Intelligence
* '''Course number:''' R-02
* '''Area of instruction:''' Computer Science and Engineering

== Course Characteristics ==

== What subject area does your course (discipline) belong to? ==

* Machine Learning
* Optimization

=== Key concepts of the class ===

* Convex Optimization
* Global Optimization
* Machine Learning and function approximation

=== What is the purpose of this course? ===

Computational Intelligence serves as a combined multidisciplinary subject, encompassing a wide range of topics. These include numeric optimization, especially convex optimization, which is the necessary and required topic for most of the modern engineering and scientific work. The course also covers global non-convex optimization methods, which are important instruments in a number of areas: product design and manufacturing, control, and others. Basic information from a number of other areas, such as machine learning, are added to complete the picture of modern intelligent computational tools that serve the same set of goals.

== Prerequisites ==

* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I CSE202] — Analytical Geometry and Linear Algebra I
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II CSE204] — Analytic Geometry And Linear Algebra II: SVD, least squares, pseudoinverse, semidefinite matrices, solving systems of linear eq., dot product, norms. Basic Geometry (ellipsoids, planes, normal, tangent).
* Basic multivariable Calculus (extremum and minimum of a function, derivatives, Jacobians)
* Programming (Python/Matlab)

== Recommendations for students on how to succeed in the course ==
How can students fill the gap?
* [https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab 3blue1brown playlist on Linear Algebra] can help to overview selected topics, or [https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/ https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/]
* Or any textbook on [https://eduwiki.innopolis.university/index.php/BSc:AnalyticGeometryAndLinearAlgebraI CSE202 — Analytical Geometry and Linear Algebra I], [https://eduwiki.innopolis.university/index.php/BSc:AnalyticGeometryAndLinearAlgebraII CSE204 — Analytic Geometry And Linear Algebra II]

== Course objectives based on Bloom’s taxonomy ==

=== What should a student remember at the end of the course? ===

By the end of the course, the students should be able to outline:

* Convexity, Convex Sets, Convex optimization
* Quadratic programming, Second Order Cone Programming, Semidefinite Programming
* Optimization in Controller design, Optimization in path planning, Optimization in Mechanical Engineering
* Nonlinear non-convex optimization, RRT algorithm, Genetic Algorithm, Particle Swarm Optimization, Function approximation.

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to understand:

* Structure of optimization problems
* Convexity criteria
* Properties of convex optimization
* Types of problems that cab be transformed into Linear Programs
* Types of problems that cab be transformed into Quadratic Programs
* Types of problems that cab be transformed into Second Order Cone Programs
* Types of problems that cab be transformed into Semidefinite Programs
* Types of non-convex problems that can be approximated by a convex relaxation
* Mixed-integer Optimization
* Path planning as an optimization
* Controller design as an optimization
* Optimal parameter choice
* RRT implementation
* PSO implementation

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to

* Design Control using Convex Optimization.
* Implement iterative non-linear controllers with inequality constraints.
* Find optimal parameter choice for processes
* proof convexity of a problem
* program optimization in CVX
* make RRT-based algorithms, understand their limitations
* make PSO-based algorithms, understand their limitations
* make GA-based algorithms, understand their limitations

=== Course evaluation ===

{|
|+ Course grade breakdown
!align="center"|
!align="center"|
!align="center"| '''Proposed points'''
|-
|align="center"| Labs/seminar classes
|align="center"| 20
|align="center"| 30
|-
|align="center"| Interim performance assessment
|align="center"| 30
|align="center"| 20
|-
|align="center"| Exams
|align="center"| 50
|align="center"| 50
|}

=== Grades range ===

{|
|+ Course grading range
!align="center"|
!align="center"|
!align="center"| '''Proposed range'''
|-
|align="center"| A. Excellent
|align="center"| 90-100
|align="center"| 85-100
|-
|align="center"| B. Good
|align="center"| 75-89
|align="center"| 70-84
|-
|align="center"| C. Satisfactory
|align="center"| 60-74
|align="center"| 50-69
|-
|align="center"| D. Poor
|align="center"| 0-59
|align="center"| 0-49
|}

=== Resources and reference material ===

Main textbooks:

* Engelbrecht, A.P., 2007. Computational intelligence: an introduction. John Wiley & Sons.
* Pedrycz, W., 1997. Computational intelligence: an introduction. CRC press.
* Konar, A., 2006. Computational intelligence: principles, techniques and applications. Springer Science & Business Media.

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{|
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Introduction to optimization methods
|align="center"| 6
|-
|align="center"| 2
| Convex Optimization
|align="center"| 6
|-
|align="center"| 3
| Global Optimization
|align="center"| 6
|}

=== Section 1 ===

==== Section title: ====

Introduction to optimization methods

=== Topics covered in this section: ===

* Optimization problem types.
* Constraint types.
* Cost function (Reward function) types.
* Practical examples of optimization problems.
* Basic optimization algorithms and their limitations.
* Lagrange multipliers.
* Gradient descent.

=== What forms of evaluation were used to test students’ performance in this section? ===

{|
!align="center"|
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 1
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 0
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Describe an engineering problem as an optimization problem.
# What is the domain of an optimization problem?
# What is the difference between Convex and Non-convex optimization?

=== Typical questions for seminar classes (labs) within this section ===

# Formulate a linear system with multi-dimensional solution space and inequality constraints.
# Solve a linear system with inequality constraints via gradient descent.

=== Section 2 ===

==== Section title: ====

Convex Optimization.

=== Topics covered in this section: ===

* Convex sets.
* Convex functions.
* Convex optimization problems.
* Properties of the convex optimizations.
* Linear Programming
* Quadratic Programming.
* Second Order Cone Programming.
* Semidefinite Programming.
* Vertical Stability of a bipedal robot as an optimization.
* Quadrotor path planning as an optimization.
* Controller design as an optimization.
* CVX.

=== What forms of evaluation were used to test students’ performance in this section? ===

{|
!align="center"|
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 1
|-
|align="center"| Testing (written or computer based)
|align="center"| 0
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Provide examples of convex problems.
# What is a convex domain?
# Examples of problems with a convex cost but non-convex domain?
# What is the difference between quadratic and conic programming?
# What is the hierarchy of convex optimization problems?
# What solvers can solve quadratic programs?
# What solvers can solve SOCP?
# What solvers can solve LP?
# What solvers can solve SDP?

=== Typical questions for seminar classes (labs) within this section ===

# What kinds of inequality constraints make the problem non-convex?
# What kinds of inequality constraints make the problem non-feasible?
# Show an example of a problem where the cost is a 2-norm. Show that it is a SOCP.
# Show an example of a problem where the cost is a 2-norm. Prove that it can be equivalently solved as a QP.
# Solve trajectory planning for a quadrotor as a SOCP.
# Solve vertical stability check for a biped as a QP.
# implement ZMP trajectory planning as a QP.
# implement LTI controller design as a SDP.

=== Section 3 ===

==== Section title: ====

Global Optimization

==== Topics covered in this section: ====

* Properties of non-convex problems.
* Problems with multiple solutions.
* Problems with weak local minima.
* Problems with computationally expensive gradients.
* Path planning on non-convex maps.
* Controller design as a non-convex problem.
* Relaxations.
* Mixed integer programming.
* PSO.
* RRT.
* Genetic Algorithm.
* Machine Learning.

=== What forms of evaluation were used to test students’ performance in this section? ===

{|
!align="center"|
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 0
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 0
|-
|align="center"| Reports
|align="center"| 1
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 0
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Provide examples of non-convex problems?
# Why non-convex problems can have multiple solutions?
# What are local minima?
# What is global optimization?
# Provide an example of a non-convex path planning problems?
# What are the limitations of PSO?

==== Typical questions for seminar classes (labs) within this section ====

# Implement PSO for a parameter optimization problem.
# Make a comparative study of PSO, GA and Random Search.
# What is the difference between random search, Sobol sequences-based methods and PSO? Show it in the numerical examples.
# Implement RRT
# Implement GA

==== Test questions for final assessment in this section ====

# Solve minimum distance to a plane problem, when the domain is non-convex.
# Find optimal controller parameters for a given trajectory of a non-linear system.
# Which non-linear algorithms can solve problems with non-convex domains?

MSc: Dynamics Of Non Linear Robotic Systems

2022-04-22T13:23:27Z

R.akhmetzyanov: /* Prerequisites */

= Dynamics of Non Linear Robotic Systems =

* '''Course name:''' Dynamics of Non Linear Robotic Systems
* '''Course number:''' R-01

== Course Characteristics ==

=== Key concepts of the class ===

Robotics; Robotic components; Robotic control.

=== What is the purpose of this course? ===

This course is an introduction to the field of robotics. It covers the fundamentals of kinematics, dynamics, and control of robot manipulators, robotic vision, and sensing. The course deals with forward and inverse kinematics of serial chain manipulators, the manipulator Jacobian, force relations, dynamics, and control. It presents elementary principles on proximity, tactile, and force sensing, vision sensors, camera calibration, stereo construction, and motion detection. The course concludes with current applications of robotics in active perception, medical robotics, autonomous vehicles, and other areas.

== Prerequisites ==

The course will benefit if students already know some topics of mathematics and programming.

* Matlab or Python
* numpy library
* Google Colab environment.
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I CSE201] — Mathematical Analysis I
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II CSE203] — Mathematical Analysis II: differentiation, exponentials, gradient.
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I1 CSE202] — Analytical Geometry and Linear Algebra I
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II CSE204] — Analytic Geometry And Linear Algebra II: matrix multiplication, change of the bases, orthonormal spaces, cross product and skew-symmetric matrices.
* Screw theory (optional).
* [https://eduwiki.innopolis.university/index.php/BSc:_Physics_I CSE402] — Physics I (Mechanics) and [https://eduwiki.innopolis.university/index.php/BSc:_Physics_II CSE410] — Physics II - Electrical Engineering: Kinematics, Statics and Dynamics.

== Recommendations for students on how to succeed in the course ==
References:
* Any text book on Linear algebra, Calculus, Programming and Physics
* [https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab 3blue1brown playlist on Linear Algebra] can help to overview selected topics.
* [https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/ Gilbert Strang] is one of the best human teachers of Algebra, if you prefer classic lectures to fancy videos.
* Kick start your numpy with the official [https://numpy.org/doc/stable/user/quickstart.html quickstart guide].
* Dimentberg, F. M. (1965) [http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=AD0680993 The Screw Calculus and Its Applications in Mechanics]

=== Course objectives based on Bloom’s taxonomy ===

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to ...

* Model the kinematics of robotic systems.
* Compute end-effector position and orientation from joint angles of a robotic system.
* Compute the joint angles of a robotic system to reach the desired end-effector position and orientation.
* Compute the linear and angular velocities of the end-effector of a robotic system from the joint angle velocities.
* Convert a robot’s workspace to its configuration space and represent obstacles in the configuration space.
* Compute valid path in a configuration space with motion planning algorithms.
* Apply the generated motion path to the robotic system to generate a proper motion trajectory.
* Apply the learned knowledge to several robotic systems: including robotic manipulators, humanoid robots.

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to ...

* Name various applications of robots
* Describe the current and potential economic and societal impacts of robot technology
* Use the Jacobian to transform velocities and forces from joint space to operational space
* Determine the singularities of a robot manipulator
* Formulate the dynamic equations of a robot manipulator in joint space and in Cartesian space
* List the major design parameters for robot manipulators and mobile robots
* List the typical sensing and actuation methods used in robots
* Analyze the workspace of a robot manipulator
* List the special requirements of haptic devices and medical robots
* Effectively communicate research results

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to ...

* Describe rigid body motions using positions, orientations, frames, and mappings
* Describe orientations using Euler angles, fixed angles, and quaternions
* Develop the forward kinematic equations for an articulated manipulator
* Describe the position and orientations of a robot in terms of joint space, Cartesian space, and operational space
* Develop the Jacobian for a specific manipulator
* Determine the singularities of a robot manipulator
* Write the dynamic equations of a robot manipulator using the Lagrangian Formulation
* Analyze the workspace of a robot manipulator

=== Course evaluation ===

{|
|+ Course grade breakdown
!
!
!align="center"| '''Proposed points'''
|-
| Weekly quizzes
| 20
|align="center"|
|-
| Home assignments
| 20
|align="center"|
|-
| Project
| 20
|align="center"|
|-
| Midterm Exam
| 20
|align="center"|
|-
| Final Exam
| 20
|align="center"|
|}

=== Grades range ===

{|
|+ Course grading range
!
!
!align="center"| '''Proposed range'''
|-
| A. Excellent
| 92-100
|align="center"|
|-
| B. Good
| 80-91
|align="center"|
|-
| C. Satisfactory
| 65-79
|align="center"|
|-
| D. Poor/Fail
| 0-59
|align="center"|
|}

=== Resources and reference material ===

* Siciliano, Sciavicco, Villani, and Oriolo, Robotics: Modeling, Planning and Control, Springe

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{|
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Introduction to robotics
|align="center"| 14
|-
|align="center"| 2
| Kinematics
|align="center"| 14
|-
|align="center"| 3
| Differential kinematics
|align="center"| 16
|-
|align="center"| 4
| Dynamics
|align="center"| 16
|}

=== Section 1 ===

==== Section title: ====

Introduction to robotics

=== Topics covered in this section: ===

* Introduction to Robotics, History of Robotics
* Introduction to Drones
* Introduction to Self driving cars
* Programming of Industrial Robot

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 1 
Homework and group projects & 1 
Midterm evaluation & 1 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# What is the difference between the manipulator arm and manipulator wrist
# What is Node in ROS
# What are the disadvantages of ROS
# Write sensors which are used in self driving cars.
# Describe the classical approach for deign self driving car

=== Typical questions for seminar classes (labs) within this section ===

# Advantages and drawbacks of robotic manipulators
# Programming industrial robots
# Developing self driving car
# Drones and controllers for them

=== Test questions for final assessment in this section ===

# Typical commands for programming industrial manipulator motions
# Types of robots and their application ares
# Control of self driving car

=== Section 2 ===

==== Section title: ====

Kinematics

=== Topics covered in this section: ===

* Rigid body and Homogeneous transformation
* Direct Kinematics
* Inverse Kinematics

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 1 
Homework and group projects & 1 
Midterm evaluation & 1 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Properties of Rotation Matrix
# How to find Euler angles from rotation matrix
# How to compute rotation matrix from knowing Euler angles
# How to derive equations for direct kinematic problem
# How to solve inverse kinematics problem

=== Typical questions for seminar classes (labs) within this section ===

# Structure, properties, and advantages of Homogeneous transformation
# Expression for rotation around an arbitrary axis
# Euler angles
# Difference between Joint and Operational spaces
# Direct kinematics for serial kinematic chain
# Piper approach for inverse kinematics

=== Test questions for final assessment in this section ===

# Transformation between reference frames
# Find Euler angles for given orientation matrix and transformation order
# Transformation between Cartesian and operational spaces
# Direct kinematic for SCARA robot
# Inverse kinematic for SCARA robot

=== Section 3 ===

==== Section title: ====

Differential kinematics

==== Topics covered in this section: ====

* Differential kinematics
* Geometric calibration
* Trajectory Planning

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 1 
Homework and group projects & 1 
Midterm evaluation & 1 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Write the matrix of differential transformation
# What is Jacobian matrix
# Difference between parametric and non-parametric robot calibration.
# Why we need complete and irreducible model
# How trajectory planning is realised
# What is trajectory junction

==== Typical questions for seminar classes (labs) within this section ====

# Jacobian matrix calculation
# Jacobian matrices for typical serial manipulators
# Robot calibration procedure
# complete, irreducible geometric model
# robot control strategies with offline errors compensation
# Trajectory planning in joint and Cartesian spaces
# Trajectory junction

==== Test questions for final assessment in this section ====

# Write Jacobian for Polarrobot
# Advantages and disadvantages parametric and non-parametric robot calibration.
# complete, irreducible geometric model for spherical manipulator
# Compute the joint trajectory q(t) from q(0) = 1 to q(2) = 4 with null initial and final velocities and accelerations. (polynomial)
# Obtain manipulator trajectory for given manipulator kinematics, initial and final states and velocity and acceleration limits/

=== Section 4 ===

==== Section title: ====

Dynamics

==== Topics covered in this section: ====

* Dynamics of Rigid body
* Lagrange approach
* Newton-Euler approach

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 1 
Homework and group projects & 1 
Midterm evaluation & 1 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Energy of rigid body
# Dynamics of rigid body
# What is Direct and Inverse Dynamics
# Difference between Newton Euler and Lagrange Euler approaches

==== Typical questions for seminar classes (labs) within this section ====

# Dynamics of rigid body
# Direct and Inverse Dynamic
# Newton-Euler Approach
# Lagrange-Euler Approach

==== Test questions for final assessment in this section ====

# Solve inverse dynamics problem for Cartesian robot
# Solve direct dynamics problem for RRR spherical manipulator
# Moving frame approach for dynamics modelling

MSc: Fundamentals of Robot Control

2022-04-22T13:22:39Z

R.akhmetzyanov: /* Prerequisites */

= Fundamentals of Robot Control =

* '''Course name:''' Fundamentals of Robot Control
* '''Course number:''' F19

== Prerequisites ==
The course will benefit if students already know some topics of mathematics and programming.
* python,
* numpy library,
* Google Colab environment
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I CSE201] — Mathematical Analysis I and CSE203 — Mathematical Analysis II]: integration and differentiation, exponentials, gradient.
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I1 CSE202] — Analytical Geometry and Linear Algebra I
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II CSE204] — Analytic Geometry And Linear Algebra II: matrix multiplication, eigenvector and eigenvalue.
* [https://eduwiki.innopolis.university/index.php/BSc:_Differential_Equations CSE205] — Differential Equations: state-space representation, homogeneous and nonhomogeneous equations, general solution of linear 1st and 2nd order ODEs, stability of solutions.
* Linear control theory: concepts of feedback, open- and closed-loop systems, linear controllers (PD, PID)

== Course Characteristics ==

=== Key concepts of the class ===

* Introductory nonlinear control over dynamic systems with the focus on robotics
* Stability, pros and cons of nonlinear control systems

=== What is the purpose of this course? ===

Control theory is an integral part of modern robotics, and there is a high chance that most students majoring in Robotics would face the problems of controlling a physical plant (a robot, manipulator, drone, autonomous vehicle) in their research and graduation work as well as their professional careers. Therefore, the main purpose of this course is to prepare the students for solving practical control problems by teaching the most fundamental approaches of nonlinear control used in modern robotics applications.

== Recommendations for students on how to succeed in the course ==

How can students fill the gap?
* [https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab 3blue1brown playlist on Linear Algebra] can help to overview selected topics.
* Most lectures of [https://youtu.be/Pi7l8mMjYVE Steve Brunton] on linear control will be useful.
* Kick start your numpy with the official [https://numpy.org/doc/stable/user/quickstart.html quickstart guide].

=== Course objectives based on Bloom’s taxonomy ===

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to remember and differentiate

* Basic structure of differential equations describing motion of robotic manipulators,
* Motivation behind and the basic structure of feedback control systems,
* How to find control system’s error dynamics and methods to analyze it,
* General structure of linear controllers (P, PD, PID),
* Physical motivation behind Lyapunov stability analysis,
* Basic structure of robust control system.

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to

* Name the main sources of nonlinearities in physical systems,
* Explain the cons of applying PID controllers to nonlinear systems,
* Understand pros and cons of feedback linearization method,
* Name pros and cons of robust control approach,
* Numerically solve differential equations in MATLAB environment.

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to ...

* Know how to analyze stability of physical systems with Lyapunov direct method,
* Design feedback linearization systems,
* Synthesize robust control systems and tune them,
* Implement nonlinear control in MATLAB environment to simulate the behavior of multi-DOF robotic systems.

=== Course evaluation ===

{|
|+ Course grade breakdown
!
!
!align="center"| '''Proposed points'''
|-
| Labs/seminar classes
| 20
|align="center"| 0
|-
| Interim performance assessment
| 30
|align="center"| 60
|-
| Exams
| 50
|align="center"| 40
|}

If necessary, please indicate freely your course’s features in terms of students’ performance assessment:

The course grades are given according to the following rules: Homework assignments (4) = 20 pts, Quizzes (4) = 40 pts, Term project = 40 pts.

=== Grades range ===

{|
|+ Course grading range
!
!
!align="center"| '''Proposed range'''
|-
| A. Excellent
| 90-100
|align="center"| 80-100
|-
| B. Good
| 75-89
|align="center"| 60-79
|-
| C. Satisfactory
| 60-74
|align="center"| 40-59
|-
| D. Poor
| 0-59
|align="center"| 0-39
|}

If necessary, please indicate freely your course’s grading features: The first year master course students come with very diverse backgrounds, and therefore the grade requirements for this course are relaxed.

=== Resources and reference material ===

The course is build based on these main textbooks:

* “Applied nonlinear control,” J.-J. Slotine & Weiping Li. Pearson, 1991.
* “Robotics: modelling, planning and control,” Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, and Giuseppe Oriolo. Springer Science & Business Media, 2010.

Other reference material:

* “Robot modeling and control,” Mark Spong, Seth Hutchinson, M. Vidyasagar. John Wiley & Sons, 2006.
* “Modern Control Systems” (13th Edition), Richard Dorf & Robert H. Bishop. Pearson, 2016.
* “Modern Robotics: Mechanics, Planning, and Control,” Kevin Lynch, Frank Park. Cambridge University Press, 2017 (''also, check out their video materials on YouTube'').

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{|
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Motion. Kinematics. Dynamics
|align="center"| 7
|-
|align="center"| 2
| Linear systems. Stability
|align="center"| 6
|-
|align="center"| 3
| Feedback control systems
|align="center"| 3
|-
|align="center"| 4
| Feedback linearization
|align="center"| 6
|-
|align="center"| 5
| Robust control
|align="center"| 8
|}

=== Section 1 ===

==== Section title: ====

Motion. Kinematics. Dynamics.

=== Topics covered in this section: ===

* Free body motion
* Manipulator position and orientation
* Homogeneous transformations
* Forward and inverse kinematics
* Kinetic and potential energy
* Euler-Lagrange equations

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Given initial and final object position and orientations, obtain the corresponding transformation matrix.
# Find the Jacobian for a given manipulator.
# For a given differential equation describing a physical system, and for given kinetic (K) and potential energies (U) do the following:
#* Show that there always exists a solution with respect to acceleration and that it is unique;
#* Demonstrate that the system’s Hamiltonian H = K + U remains constant in the absence of external torques and forces;
#* Show that the rate of change of the total energy equals instantaneous mechanical power.
# For a given manipulator with known Jacobian, do the following:
#* Find kinetic and potential energies of the robot;
#* Drive the Euler-Lagrange differential equation of motion.

=== Typical questions for seminar classes (labs) within this section ===

# Do the following with MATLAB Robotics Toolbox:
#* Compute basic rotation and homogeneous transformation matrices;
#* Create a two-link manipulator and solve its forward kinematics;
#* Simulate forward dynamics;
#* Compute inertial, Coriolis and gravitational forces for the manipulator.
# Compute and analyze inertial tensor for a given robotic manipulator;
# For manipulators with different kinematic configurations, analytically derive their Jacobian matrices;
# Derive and analyze Euler-Lagrange equations describing dynamics of a given manipulator (required torques, singularities).

=== Test questions for final assessment in this section ===

# What is a rotation matrix and what does it describe?
# How to find a homogeneous transformation matrix? How is it different from rotation matrix?
# What is manipulator Jacobian? How does it relate static forces and torques? How can one use the Jacobian to analyze manipulator singularities?
# What is physical nature of the terms of Euler-Lagrange equations of robot motion?
# What are the main properties of the basic terms of differential equations of motion (invertibility, positive definiteness, singularities, limits).

=== Section 2 ===

==== Section title: ====

Linear systems. Stability

=== Topics covered in this section: ===

* State-space equations
* Eigenvalues and eigenvectors
* Phase plane analysis
* Energy and stability
* Lyapunov’s direct method
* Lyapunov stability analysis

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 1 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Convert given differential equation into state-space form
# Find eigenvalue for a given matrix
# Show that given a constant matrix <math display="inline">\textbf{M}</math> and any time-varying vector <math display="inline">\textbf{x}</math>, the time derivative of the scalar <math display="inline">\mathbf{x}^T\mathbf{Mx}</math> can be written in the given form.
# For a given system of differential equations:
#* Find equilibria points;
#* Given a Lyapunov function, analyze system stability using Lyapunov’s direct method;
#* Analyze system stability in a given range.

=== Typical questions for seminar classes (labs) within this section ===

# For a given differential equation, find the values of coefficients <math display="inline">k_1</math> and <math display="inline">k_2</math> that would make it critically damped.
# Convert a given system of differential equations into state-space form.
# For an equation given in the state space form, write it as an ordinary differential equation for specified input and output variables.
# Analyze system’s stability given equation of its full energy.
# Apply Lyapunov’s direct method to analyze stability of a physical system.

=== Test questions for final assessment in this section ===

# Evaluate stability of a linear system whose dynamics is written in the state-space form.
# What must be the properties of Lyapunov function candidate and its time derivative to confirm
#* Local stability,
#* Global stability of the system.
# How to analyze system’s stability based on its phase portrait (with examples)?
# Describe physical motivation behind Lyapunov’s direct method and how it used to analyze stability of dynamical systems.

=== Section 3 ===

==== Section title: ====

Feedback control systems

==== Topics covered in this section: ====

* Feedback and building control loops
* Stabilization and trajectory tracking
* Linear regulators (P, PD, PID)

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 0 
Midterm evaluation & 0 
Testing (written or computer based) & 0 
Reports & 0 
Essays & 0 
Oral polls & 1 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Give an example of using feedback in activities of daily life
# Drive error dynamics equations for a given feedback control law
# What are the physical analogies for the individual terms of PD-controller?
# How to implement PD regulator in MATLAB software?

==== Typical questions for seminar classes (labs) within this section ====

# Solve numerically in MATLAB a second-order ODE for the following controller types: P, PD, PID.
# Analyze stability of a given linear control system.
# How individual gains of PD and PID controllers affect transient and steady-state response?
# How does underestimation of system parameters affect performance of linear controllers and how to improve it?

==== Test questions for final assessment in this section ====

# What is the physical analog of PD-regulator in application to control over second-oder mechanical systems?
# For a given system described by differential equations, design a linear control system and analyze its stability.
# Describe pros and cons of linear controllers in application to nonlinear system control.

=== Section 4 ===

==== Section title: ====

Feedback linearization

==== Topics covered in this section: ====

* Joint-space inverse dynamics of serial manipulators
* Stabilization and trajectory tracking problems
* Input-state linearization

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 1 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# For a given differential equation that describes pendulum dynamicsm do the following:
#* Find control law transforming original dynamics into that of a linear mass-spring-damper system;
#* Write position error dynamics for the designed control law (inverse dynamics);
#* Repeat the previous steps if there are uncertainties in some of the system’s parameters.
# For a given general form of dynamics equation, demonstrate that the following control laws guarantee system stability.
# Simulate dynamics of given nonlinear system in MATLAB for given control law.
# Perform input-state linearization for a given system of differential equations.

==== Typical questions for seminar classes (labs) within this section ====

# Find control law that linearizes a given differential equation.
# Analyze stability of given nonlinear systems and contribution of individual terms to system stability.
# Find general feedback linearization law for a given system of differential equations.
# Analyze stability and limitations of a given feedback linearization control law over a two-link manipulator.

==== Test questions for final assessment in this section ====

# What are the pros and cons of feedback linearization approach?
# Provide examples of systems (differential equations) for which feedback linearization can result in infinite control effort.
# What are the typical issues when applying feedback linearization approach to control over robotic manipulators?

=== Section 5 ===

==== Section title: ====

Robust control

==== Topics covered in this section: ====

* Sliding modes in dynamic systems
* Robust control in scalar and matrix form
* Stability and tuning of robust controllers
* Control law smoothening.

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 0 
Reports & 1 
Essays & 0 
Oral polls & 1 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Name uncertainties typical for mechanical systems
# Analyze system behavior in sliding mode
# Simulate system behavior with robust control in MATLAB
# Numerically implement control law smoothening for a robust controller.

==== Typical questions for seminar classes (labs) within this section ====

# How to synthesize robust controller for a given range of system parameters’ deviations?
# Describe the effect of robust gain coefficient <math display="inline">k</math> on system stability, control output and resulting system behavior.
# Design robust controller for a dynamical system described by a given set of differential equations and implement it in MATLAB.
# Analyze stability of robust control system for a robotic manipulator with given equation of motion.

==== Test questions for final assessment in this section ====

# Describe typical sources of uncertainties and parameter deviations in models of physical and mechanical systems, their typical ranges and influence on system behavior.
# Name pros and cons of robust control systems with and without control law smoothening.
# How to tune robust controller terms to compensate for system uncertainties?

MSc: Advanced Information Retrieval

2022-04-22T13:21:22Z

R.akhmetzyanov: /* Prerequisites */

= Advanced Information Retrieval =

* '''Course name:''' Advanced Information Retrieval
* '''Course number:''' N/A

== Course Characteristics ==

== What subject area does your course (discipline) belong to? ==

Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web

=== Key concepts of the class ===

* Data indexing
* Recommendations
* Relevance and ranking

=== What is the purpose of this course? ===

The course is designed to prepare students to understand and learn contemporary tools of information retrieval systems. Students, who will later dedicate their engineering or scientific careers to implementation of search engines, social networks, recommender systems and other content services will obtain necessary knowledge and skills in designing and implementing essential parts of such systems.

== Prerequisites ==

* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming CSE101] — Introduction to Programming I
* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming_II CSE102] — Introduction to Programming II
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I1 CSE202] — Analytical Geometry and Linear Algebra I
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II CSE204] — Analytic Geometry And Linear Algebra II: matrix multiplication, matrix decomposition (SVD, ALS) and approximation (matrix norm), sparse matrix, stability of solution (decomposition), vector spaces, metric spaces, manifold, eigenvector and eigenvalue.
* [https://eduwiki.innopolis.university/index.php/BSc:Logic_and_Discrete_Mathematics CSE113] — Philosophy I - (Discrete Math and Logic): graphs, trees, binary trees, balanced trees, metric (proximity) graphs, diameter, clique, path, shortest path.
* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics CSE206 — Probability And Statistics]: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.
* Analysis: DFT, [discrete] gradient.

How can students fill the gap?

* [https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab 3blue1brown playlist on Linear Algebra] can help to overview selected topics.
* Actually, on their channel you can find almost any maths topic, e.g. [https://www.youtube.com/watch?v=spUNpyF58BY Fourier Transform].
* Gilbert Strang is one of the best human teachers of Algebra, if you prefer classic lectures to fancy videos.
* [https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/ This MIT course] can help you with discrete objects.
* For Russian readers there is a [http://sprotasov.ru/math_book.html maths book from the course author] with [https://github.com/str-anger/math-book labs].
* Also there is a [https://github.com/hsu-ai-course/mbp/tree/master/notebooks very basic python-based course on maths] with lots of relevant (and irrelevant) labs.
* To have a better feeling of networking, please refer to [https://www.youtube.com/watch?v=7pyKSxWBDN0 this video lecture].
* Kick start your numpy with the official [https://numpy.org/doc/stable/user/quickstart.html quickstart guide].

=== Course objectives based on Bloom’s taxonomy ===

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to remember and recognize

* Terms and definitions used in area of information retrieval,
* Search engine and recommender system essential parts,
* Quality metrics of information retrieval systems,
* Contemporary approaches to semantic data analysis,
* Indexing strategies.

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to describe and explain

* How to design a recommender system from scratch,
* How to evaluate quality of a particular information retrieval system,
* Core ideas and system implementation and maintenance,
* How to identify and fix information retrieval system problems.

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to

* Build a recommender service from scratch,
* Implement proper index for an unstructured dataset,
* Plan quality measures for a new recommender service,
* Run initial data analysis and problem evaluation for a business task, related to information retrieval.

=== Course evaluation ===

{|
|+ Course grade breakdown
!align="center"|
!align="center"|
!align="center"| '''Proposed points'''
|-
|align="center"| Labs/seminar classes
|align="center"| 20
|align="center"| 30
|-
|align="center"| Interim performance assessment
|align="center"| 30
|align="center"| 0
|-
|align="center"| Assessments (homework)
|align="center"| 0
|align="center"| 70
|-
|align="center"| Exams
|align="center"| 50
|align="center"| 0
|}

7 hometasks will cost you up to 70 points in total (10 points each). 7 contest labs can bring you up to 5 points each. Work in teams up to 3, you will get +2 points for each successful completion and +3 points for each submission in top 3.

=== Exam and retake planning ===

'''Exam'''

No exam.

'''Retake 1'''

First retake is conducted in a form of project defense. Student is given a week to prepare. Student takes any technical paper from Information Retrieval Journal (https://www.springer.com/journal/10791) for '''the last 3 years''' and approves it until the next day with a professor to avoid collisions and misunderstanding. Student implements the paper in a search engine (this can be a technique, metric, ...). At the retake day student presents a paper. Presentation is followed by QA session. After QA session student presents implementation of the paper. Grading criteria as follows:

* 30% – paper presentation is clear, discussion of results is full.
* 30% – search engine implementation is correct and clear. Well-structured and dedicated to a separate service.
* 30% – paper implementation is correct.

'''Retake 2'''

Second retake is conducted in front of the committee. Four (4) questions are randomly selected for a student: two (2) theoretical from "Test questions for final assessment in this section" and two (2) practical from "Typical questions for ongoing performance evaluation". Each question costs 25% of the grade. Student is given 15 minutes to prepare for theoretical questions. Then (s)he answers in front of the committee. After this student if given additional 40 minutes to solve practical questions.

=== Grades range ===

{|
|+ Course grading range
!align="center"|
!align="center"|
!align="center"| '''Proposed range'''
|-
|align="center"| A. Excellent
|align="center"| 90-100
|align="center"| 84-100
|-
|align="center"| B. Good
|align="center"| 75-89
|align="center"| 70-83
|-
|align="center"| C. Satisfactory
|align="center"| 60-74
|align="center"| 60-69
|-
|align="center"| D. Poor
|align="center"| 0-59
|align="center"| 0-59
|}

=== Resources and reference material ===

Main textbook:

* "An Introduction to Information Retrieval" by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Cambridge University Press (any edition)

Other reference material:

* “Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit,” Steven Bird, Ewan Klein, and Edward Loper. [https://www.nltk.org/book/ [link]]

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{|
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Introduction. Crawling and quality basics
|align="center"| 16
|-
|align="center"| 2
| Text indexing and language processing
|align="center"| 20
|-
|align="center"| 3
| Advanced index data structures
|align="center"| 8
|-
|align="center"| 4
| Advanced retrieval topics. Media retrieval
|align="center"| 16
|}

=== Section 1 ===

==== Section title: ====

Introduction. Crawling and quality basics

=== Topics covered in this section: ===

* Introduction to information retrieval
* Crawling
* Quality assessment

=== What forms of evaluation were used to test students’ performance in this section? ===

{|
!align="center"|
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 1
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 0
|-
|align="center"| Reports
|align="center"| 0
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 0
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Enumerate limitations for web crawling.
# Propose a strategy for A/B testing.
# Propose recommender quality metric.
# Implement DCG metric.
# Discuss relevance metric.
# Crawl website with respect to robots.txt.

==== Typical questions for seminar classes (labs) within this section ====

# Show how to parse a dynamic web page.
# Provide a framework to accept/reject A/B testing results.
# Compute DCG for an example query for random search engine.
# Implement a metric for a recommender system.
# Implement pFound.

==== Test questions for final assessment in this section ====

# Implement text crawler for a news site.
# What is SBS (side-by-side) and how is it used in search engines?
# Compare pFound with CTR and with DCG.
# Explain how A/B testing works.

=== Section 2 ===

==== Section title: ====

Text indexing and language processing

=== Topics covered in this section: ===

* Building inverted index for text documents. Boolean retrieval model.
* Language, tokenization, stemming, searching, scoring.
* Spellchecking.
* Language model. Topic model.
* Vector model for texts.
* ML for text embedding.

=== What forms of evaluation were used to test students’ performance in this section? ===

{|
!align="center"|
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 1
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 0
|-
|align="center"| Reports
|align="center"| 0
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 0
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Build inverted index for a text.
# Tokenize a text.
# Implement simple spellchecker.
# Embed the text with a model.

=== Typical questions for seminar classes (labs) within this section ===

# Build inverted index for a set of web pages.
# build a distribution of stems/lexemes for a text.
# Choose and implement persistent index for a given text collection.
# Visualize a dataset for text classification.

=== Test questions for final assessment in this section ===

# Explain how (and why) KD-trees work.
# What are weak places of inverted index?
# Compare different text vectorization approaches.
# Compare tolerant retrieval to spellchecking.

=== Section 3 ===

==== Section title: ====

Advanced index data structures

==== Topics covered in this section: ====

* Vector-based tree data structures.
* Graph-based data structures. Inverted index and multi-index.

=== What forms of evaluation were used to test students’ performance in this section? ===

{|
!align="center"|
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 1
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 0
|-
|align="center"| Reports
|align="center"| 0
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 0
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Build kd-tree index for a given dataset.
# Why kd-trees work badly in 100-dimensional environment?
# What is the difference between metric space and vector space?

==== Typical questions for seminar classes (labs) within this section ====

# Build (H)NSW index for a dataset.
# Compare HNSW to Annoy index.
# What are metric space index structures you know?

==== Test questions for final assessment in this section ====

# Compare inverted index to HNSW in terms of speed, memory consumption?
# Choose the best index for a given dataset.
# Implement range search in KD-tree.

=== Section 4 ===

==== Section title: ====

Advanced retrieval topics. Media retrieval

==== Topics covered in this section: ====

* Image and video processing
* Image understanding
* Video understanding
* Audio processing
* Speech-to-text
* Relevance feedback
* PageRank

=== What forms of evaluation were used to test students’ performance in this section? ===

{|
!align="center"|
!align="center"| '''Yes/No'''
|-
|align="center"| Development of individual parts of software product code
|align="center"| 1
|-
|align="center"| Homework and group projects
|align="center"| 1
|-
|align="center"| Midterm evaluation
|align="center"| 0
|-
|align="center"| Testing (written or computer based)
|align="center"| 0
|-
|align="center"| Reports
|align="center"| 0
|-
|align="center"| Essays
|align="center"| 0
|-
|align="center"| Oral polls
|align="center"| 0
|-
|align="center"| Discussions
|align="center"| 0
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Extract semantic information from images.
# Build an image hash.
# Build a spectral representation of a song.
# Whats is relevance feedback?

==== Typical questions for seminar classes (labs) within this section ====

# Build a "search by color" feature.
# Extract scenes from video.
# Write a voice-controlled search.
# Semantic search within unlabelled image dataset.

==== Test questions for final assessment in this section ====

# What are the approaches to image understanding?
# How to cluster a video into scenes and shots?
# How speech-to-text technology works?
# How to build audio fingerprints?

MSc: Fundamentals of Robot Control

2022-04-21T16:11:49Z

R.akhmetzyanov: /* Fundamentals of Robot Control */

= Fundamentals of Robot Control =

* '''Course name:''' Fundamentals of Robot Control
* '''Course number:''' F19

== Prerequisites ==
The course will benefit if students already know some topics of mathematics and programming.
* python,
* numpy library,
* Google Colab environment
* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I CSE201] — Mathematical Analysis I and CSE203 — Mathematical Analysis II]: integration and differentiation, exponentials, gradient.
* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I1 CSE202] — Analytical Geometry and Linear Algebra I and [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II CSE204] — Analytic Geometry And Linear Algebra II: matrix multiplication, eigenvector and eigenvalue.
* [https://eduwiki.innopolis.university/index.php/BSc:_Differential_Equations CSE205] — Differential Equations: state-space representation, homogeneous and nonhomogeneous equations, general solution of linear 1st and 2nd order ODEs, stability of solutions.
* Linear control theory: concepts of feedback, open- and closed-loop systems, linear controllers (PD, PID)

== Course Characteristics ==

=== Key concepts of the class ===

* Introductory nonlinear control over dynamic systems with the focus on robotics
* Stability, pros and cons of nonlinear control systems

=== What is the purpose of this course? ===

Control theory is an integral part of modern robotics, and there is a high chance that most students majoring in Robotics would face the problems of controlling a physical plant (a robot, manipulator, drone, autonomous vehicle) in their research and graduation work as well as their professional careers. Therefore, the main purpose of this course is to prepare the students for solving practical control problems by teaching the most fundamental approaches of nonlinear control used in modern robotics applications.

== Recommendations for students on how to succeed in the course ==

How can students fill the gap?
* [https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab 3blue1brown playlist on Linear Algebra] can help to overview selected topics.
* Most lectures of [https://youtu.be/Pi7l8mMjYVE Steve Brunton] on linear control will be useful.
* Kick start your numpy with the official [https://numpy.org/doc/stable/user/quickstart.html quickstart guide].

=== Course objectives based on Bloom’s taxonomy ===

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to remember and differentiate

* Basic structure of differential equations describing motion of robotic manipulators,
* Motivation behind and the basic structure of feedback control systems,
* How to find control system’s error dynamics and methods to analyze it,
* General structure of linear controllers (P, PD, PID),
* Physical motivation behind Lyapunov stability analysis,
* Basic structure of robust control system.

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to

* Name the main sources of nonlinearities in physical systems,
* Explain the cons of applying PID controllers to nonlinear systems,
* Understand pros and cons of feedback linearization method,
* Name pros and cons of robust control approach,
* Numerically solve differential equations in MATLAB environment.

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to ...

* Know how to analyze stability of physical systems with Lyapunov direct method,
* Design feedback linearization systems,
* Synthesize robust control systems and tune them,
* Implement nonlinear control in MATLAB environment to simulate the behavior of multi-DOF robotic systems.

=== Course evaluation ===

{|
|+ Course grade breakdown
!
!
!align="center"| '''Proposed points'''
|-
| Labs/seminar classes
| 20
|align="center"| 0
|-
| Interim performance assessment
| 30
|align="center"| 60
|-
| Exams
| 50
|align="center"| 40
|}

If necessary, please indicate freely your course’s features in terms of students’ performance assessment:

The course grades are given according to the following rules: Homework assignments (4) = 20 pts, Quizzes (4) = 40 pts, Term project = 40 pts.

=== Grades range ===

{|
|+ Course grading range
!
!
!align="center"| '''Proposed range'''
|-
| A. Excellent
| 90-100
|align="center"| 80-100
|-
| B. Good
| 75-89
|align="center"| 60-79
|-
| C. Satisfactory
| 60-74
|align="center"| 40-59
|-
| D. Poor
| 0-59
|align="center"| 0-39
|}

If necessary, please indicate freely your course’s grading features: The first year master course students come with very diverse backgrounds, and therefore the grade requirements for this course are relaxed.

=== Resources and reference material ===

The course is build based on these main textbooks:

* “Applied nonlinear control,” J.-J. Slotine & Weiping Li. Pearson, 1991.
* “Robotics: modelling, planning and control,” Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, and Giuseppe Oriolo. Springer Science & Business Media, 2010.

Other reference material:

* “Robot modeling and control,” Mark Spong, Seth Hutchinson, M. Vidyasagar. John Wiley & Sons, 2006.
* “Modern Control Systems” (13th Edition), Richard Dorf & Robert H. Bishop. Pearson, 2016.
* “Modern Robotics: Mechanics, Planning, and Control,” Kevin Lynch, Frank Park. Cambridge University Press, 2017 (''also, check out their video materials on YouTube'').

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{|
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Motion. Kinematics. Dynamics
|align="center"| 7
|-
|align="center"| 2
| Linear systems. Stability
|align="center"| 6
|-
|align="center"| 3
| Feedback control systems
|align="center"| 3
|-
|align="center"| 4
| Feedback linearization
|align="center"| 6
|-
|align="center"| 5
| Robust control
|align="center"| 8
|}

=== Section 1 ===

==== Section title: ====

Motion. Kinematics. Dynamics.

=== Topics covered in this section: ===

* Free body motion
* Manipulator position and orientation
* Homogeneous transformations
* Forward and inverse kinematics
* Kinetic and potential energy
* Euler-Lagrange equations

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 0 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Given initial and final object position and orientations, obtain the corresponding transformation matrix.
# Find the Jacobian for a given manipulator.
# For a given differential equation describing a physical system, and for given kinetic (K) and potential energies (U) do the following:
#* Show that there always exists a solution with respect to acceleration and that it is unique;
#* Demonstrate that the system’s Hamiltonian H = K + U remains constant in the absence of external torques and forces;
#* Show that the rate of change of the total energy equals instantaneous mechanical power.
# For a given manipulator with known Jacobian, do the following:
#* Find kinetic and potential energies of the robot;
#* Drive the Euler-Lagrange differential equation of motion.

=== Typical questions for seminar classes (labs) within this section ===

# Do the following with MATLAB Robotics Toolbox:
#* Compute basic rotation and homogeneous transformation matrices;
#* Create a two-link manipulator and solve its forward kinematics;
#* Simulate forward dynamics;
#* Compute inertial, Coriolis and gravitational forces for the manipulator.
# Compute and analyze inertial tensor for a given robotic manipulator;
# For manipulators with different kinematic configurations, analytically derive their Jacobian matrices;
# Derive and analyze Euler-Lagrange equations describing dynamics of a given manipulator (required torques, singularities).

=== Test questions for final assessment in this section ===

# What is a rotation matrix and what does it describe?
# How to find a homogeneous transformation matrix? How is it different from rotation matrix?
# What is manipulator Jacobian? How does it relate static forces and torques? How can one use the Jacobian to analyze manipulator singularities?
# What is physical nature of the terms of Euler-Lagrange equations of robot motion?
# What are the main properties of the basic terms of differential equations of motion (invertibility, positive definiteness, singularities, limits).

=== Section 2 ===

==== Section title: ====

Linear systems. Stability

=== Topics covered in this section: ===

* State-space equations
* Eigenvalues and eigenvectors
* Phase plane analysis
* Energy and stability
* Lyapunov’s direct method
* Lyapunov stability analysis

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 1 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Convert given differential equation into state-space form
# Find eigenvalue for a given matrix
# Show that given a constant matrix <math display="inline">\textbf{M}</math> and any time-varying vector <math display="inline">\textbf{x}</math>, the time derivative of the scalar <math display="inline">\mathbf{x}^T\mathbf{Mx}</math> can be written in the given form.
# For a given system of differential equations:
#* Find equilibria points;
#* Given a Lyapunov function, analyze system stability using Lyapunov’s direct method;
#* Analyze system stability in a given range.

=== Typical questions for seminar classes (labs) within this section ===

# For a given differential equation, find the values of coefficients <math display="inline">k_1</math> and <math display="inline">k_2</math> that would make it critically damped.
# Convert a given system of differential equations into state-space form.
# For an equation given in the state space form, write it as an ordinary differential equation for specified input and output variables.
# Analyze system’s stability given equation of its full energy.
# Apply Lyapunov’s direct method to analyze stability of a physical system.

=== Test questions for final assessment in this section ===

# Evaluate stability of a linear system whose dynamics is written in the state-space form.
# What must be the properties of Lyapunov function candidate and its time derivative to confirm
#* Local stability,
#* Global stability of the system.
# How to analyze system’s stability based on its phase portrait (with examples)?
# Describe physical motivation behind Lyapunov’s direct method and how it used to analyze stability of dynamical systems.

=== Section 3 ===

==== Section title: ====

Feedback control systems

==== Topics covered in this section: ====

* Feedback and building control loops
* Stabilization and trajectory tracking
* Linear regulators (P, PD, PID)

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 0 
Midterm evaluation & 0 
Testing (written or computer based) & 0 
Reports & 0 
Essays & 0 
Oral polls & 1 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Give an example of using feedback in activities of daily life
# Drive error dynamics equations for a given feedback control law
# What are the physical analogies for the individual terms of PD-controller?
# How to implement PD regulator in MATLAB software?

==== Typical questions for seminar classes (labs) within this section ====

# Solve numerically in MATLAB a second-order ODE for the following controller types: P, PD, PID.
# Analyze stability of a given linear control system.
# How individual gains of PD and PID controllers affect transient and steady-state response?
# How does underestimation of system parameters affect performance of linear controllers and how to improve it?

==== Test questions for final assessment in this section ====

# What is the physical analog of PD-regulator in application to control over second-oder mechanical systems?
# For a given system described by differential equations, design a linear control system and analyze its stability.
# Describe pros and cons of linear controllers in application to nonlinear system control.

=== Section 4 ===

==== Section title: ====

Feedback linearization

==== Topics covered in this section: ====

* Joint-space inverse dynamics of serial manipulators
* Stabilization and trajectory tracking problems
* Input-state linearization

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 1 
Reports & 0 
Essays & 0 
Oral polls & 1 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# For a given differential equation that describes pendulum dynamicsm do the following:
#* Find control law transforming original dynamics into that of a linear mass-spring-damper system;
#* Write position error dynamics for the designed control law (inverse dynamics);
#* Repeat the previous steps if there are uncertainties in some of the system’s parameters.
# For a given general form of dynamics equation, demonstrate that the following control laws guarantee system stability.
# Simulate dynamics of given nonlinear system in MATLAB for given control law.
# Perform input-state linearization for a given system of differential equations.

==== Typical questions for seminar classes (labs) within this section ====

# Find control law that linearizes a given differential equation.
# Analyze stability of given nonlinear systems and contribution of individual terms to system stability.
# Find general feedback linearization law for a given system of differential equations.
# Analyze stability and limitations of a given feedback linearization control law over a two-link manipulator.

==== Test questions for final assessment in this section ====

# What are the pros and cons of feedback linearization approach?
# Provide examples of systems (differential equations) for which feedback linearization can result in infinite control effort.
# What are the typical issues when applying feedback linearization approach to control over robotic manipulators?

=== Section 5 ===

==== Section title: ====

Robust control

==== Topics covered in this section: ====

* Sliding modes in dynamic systems
* Robust control in scalar and matrix form
* Stability and tuning of robust controllers
* Control law smoothening.

=== What forms of evaluation were used to test students’ performance in this section? ===

<div class="tabular">

|a|c| & '''Yes/No''' 
Development of individual parts of software product code & 0 
Homework and group projects & 1 
Midterm evaluation & 0 
Testing (written or computer based) & 0 
Reports & 1 
Essays & 0 
Oral polls & 1 
Discussions & 1 

</div>
=== Typical questions for ongoing performance evaluation within this section ===

# Name uncertainties typical for mechanical systems
# Analyze system behavior in sliding mode
# Simulate system behavior with robust control in MATLAB
# Numerically implement control law smoothening for a robust controller.

==== Typical questions for seminar classes (labs) within this section ====

# How to synthesize robust controller for a given range of system parameters’ deviations?
# Describe the effect of robust gain coefficient <math display="inline">k</math> on system stability, control output and resulting system behavior.
# Design robust controller for a dynamical system described by a given set of differential equations and implement it in MATLAB.
# Analyze stability of robust control system for a robotic manipulator with given equation of motion.

==== Test questions for final assessment in this section ====

# Describe typical sources of uncertainties and parameter deviations in models of physical and mechanical systems, their typical ranges and influence on system behavior.
# Name pros and cons of robust control systems with and without control law smoothening.
# How to tune robust controller terms to compensate for system uncertainties?

MSc: Large Systems

2022-04-21T14:57:45Z

R.akhmetzyanov: /* Prerequisites */

== Prerequisites ==
The course has been designed to be self-included as much as possible. The successful completion will depend on prerequisite courses such as
* Essential skills
* [https://eduwiki.innopolis.university/index.php/MSc:_Classical_Internet_Applications CSE517 - Classical Internet Applications]
* [https://eduwiki.innopolis.university/index.php/MSc:_Inter_Networking_and_Routing CSE518 - Internetworking And Routing]

MSc: Advanced Statistics

2022-04-21T14:44:58Z

R.akhmetzyanov: /* Prerequisites */

= Advanced Statistics =

* '''Course name:''' Advanced Statistics
* '''Course number:''' DS-03

== Prerequisites ==
This course will benefit from good English language skills. Also it could be great if you acquire basic statistical skills in the perspective of [https://en.wikipedia.org/wiki/Empirical_research Empirical research]: statistical hypothesis testing, dependent and independent variables, distributions, etc..
* [https://eduwiki.innopolis.university/index.php/MSc:_Empirical_Methods CSE329 - Empirical Methods]

== Course Characteristics ==
=== Key concepts of the class ===

* Statistical inference
* Non parametric statistics
* Test of statistical hypotheses
* Simple linear regression and correlation analysis
* Meta-Analysis

=== What is the purpose of this course? ===

The main purpose of this course is to present the fundamentals of inferential statistics to the future software engineers and data scientists, on one side providing the scientific fundamentals of the disciplines, and on the other anchoring the theoretical concepts on practices coming from the world of software development and engineering. The course covers the statistical analysis of data with limited assumptions on the distribution, with reference to testing hypotheses, measuring correlations, building samples, and performing regressions.

=== Course objectives based on Bloom’s taxonomy ===

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to:

* Remember the fundamentals of inferential statistics
* Remember the specifics and purpose of different hypothesis tests
* Distinguish between parametric and non parametric tests

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to understand:

* the basic concepts of inferential statistics
* the fundamental laws in statistics
* the concept of null and alternative hypotheses
* the hypotheses test procedure

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to ...

* To understand the problems related to analyse statistically data not distributed normally
* To know the more recent computationally-intensive techniques that can help to describe samples and to infer properties of populations in absence of normality
* To identify situations when the data is on nominal scales so alternative techniques should be use, and act accordingly.
* To be able to run experiment to evaluate hypotheses for situation of scarce data, distributed non normally, on different kinds of scales.

=== Course evaluation (Standard) ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grade breakdown
!
!
!align="center"| '''Points'''
|-
| Weekly quizzes
|
|align="center"| 10
|-
| Midterm
|
|align="center"| 20
|-
| Final oral exam
|
|align="center"| 35
|-
| Final written exam
|
|align="center"| 30
|-
| Participation
|
|align="center"| 5
|}

=== Course evaluation (Project Based) ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grade breakdown
!
!
!align="center"| '''Points'''
|-
| Weekly quizzes
|
|align="center"| 15
|-
| Weekly Projects Review
|
|align="center"| 15
|-
| Mid of Semester Project Review
|
|align="center"| 20
|-
| Final Report
|
|align="center"| 30
|-
| Final Presentation with Q&A
|
|align="center"| 20
|}

=== Grades range ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grading range
!
!
!align="center"| '''Range'''
|-
| A. Excellent
|
|align="center"| 95-100
|-
| B. Good
|
|align="center"| 75-94
|-
| C. Satisfactory
|
|align="center"| 55-74
|-
| D. Poor
|
|align="center"| 0-54
|}

=== Cooperation policy and quotations ===
We encourage vigorous discussion and cooperation in this class. You should feel free to discuss any aspects of the class with any classmates. However, we insist that any written material that is not specifically designated as a Team Deliverable be done by you alone. This includes answers to reading questions, individual reports associated with assignments, and labs. We also insist that if you include verbatim text from any source, you clearly indicate it using standard conventions of quotation or indentation and a note to indicate the source.

=== Resources and reference material ===

* Wasserman L. (2006) All of Nonparametric Statistics. Springer
* Randles, R.H. and Wolfe, D.A. (1991). Introduction to the Theory of Nonparametric Statistics. Melbourne: Robert Krieger. (Ch.1‐Ch.4)
* Hastie, T. Tibshirani, R. and Friedman, J. (2008) The Elements of Statistical Learning 2ed. Springer
* Hollander, M. and Wolfe, D.A. (1999). Nonparametric Statistical Methods, 2nd ed. New York: John Wiley.

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Sampling Distributions Associated with the Normal Population
|align="center"| 15
|-
|align="center"| 2
| Test of Statistical Hypotheses
|align="center"| 30
|-
|align="center"| 3
| Simple Linear Regression and Correlation Analysis
|align="center"| 15
|}

=== Section 1 ===

==== Section title: ====

Sampling Distributions Associated with the Normal Population

=== Topics covered in this section: ===

* Introduction to the course, toward inference
* Student’s t-distribution
* Bernoulli and binomial distribution
* Chi-square distribution
* Snedecor’s F-distribution

=== What forms of evaluation were used to test students’ performance in this section? ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
!align="center"| '''Evaluation'''
!align="center"| '''Yes/No'''
|-
| Development of individual parts of software product code
|align="center"| 0
|-
| Homework and group projects
|align="center"| 0
|-
| Midterm evaluation
|align="center"| 1
|-
| Testing (written or computer based)
|align="center"| 1
|-
| Reports
|align="center"| 0
|-
| Essays
|align="center"| 0
|-
| Oral polls
|align="center"| 0
|-
| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Deduce the probability mass function <math display="inline">P(X \leq k</math> for a binomial distribution?
# Let X1,...,Xk be ''k'' iid random variables distributed with a <math display="inline">\chi^2</math> distribution with n1,...nk degrees of freedom respectively. 
What is the distribution of Y=X1+...+Xk? Define it precisely and prove the answer formally?
# List at least 3 random variables that “tend to follow” a t distribution?
# If X has Chi square function with the 5 degrees of freedom, then what is the probability that X is between 1.145 and 12.83?
# If X has a gamma distribution of (1,1), then what is the probability density function of the random variable 2X?

=== Typical questions for seminar classes (labs) within this section ===

# Define and provide examples of sample space, events and probability measure.
# Write the formula for the coefficients of the simple linear regression. Explain the mathematical procedure you do to derive them and derive them.
# Calculate the correlation between two functions and explain its meaning.
# Calculate the Pearson coefficient for the given functions.
# Deduce the MGF for normal distribution.
# State and prove the Bonferroni inequality.

=== Test questions for final assessment in this section ===

== Test of Statistical Hypotheses ==

=== Topics covered in this section: ===

* Z-test
* Student’s t-test
* Chi-square test
* Snedecor’s F-test

=== What forms of evaluation were used to test students’ performance in this section? ===

=== Typical questions for ongoing performance evaluation within this section ===

# Define the concept of power of a statistical test.
# Define the purpose of the F Test, its hypotheses, and its structure.
# Define the purpose of the t-Test, its hypotheses, and its structure.
# Define the purpose of the Chi square Test, its hypotheses, and its structure.
# Define the purpose of the Z Test, its hypotheses, and its structure.
# Provide concrete numeric examples with explanation on why the power of a test depends on:
## the size of the data sets.
## the magnitude of the effect.
## the level of statistical significance.
# Given a statistical test for which we have set a value <math display="inline">\alpha</math> we obtain a p:
## if we can reject H0 <math display="inline">(p < \alpha)</math>, what we typically say about H0 and H1.
## if we cannot reject H0 <math display="inline">(P \geq \alpha)</math>, what we can typically say about H0 and H1.
## when can we say that H0 holds?
## when can we say that H1 holds?

=== Typical questions for seminar classes (labs) within this section ===

# Provide a concrete example of a t test, detailing both H0 and H1.
# Present the structure of the F test for the analysis of the variance.
# Explain what are H0 and H1 in hypothesis testing.
# Explain the role of the Bonferroni inequality in hypothesis testing.

=== Test questions for final assessment in this section ===

== Simple Linear Regression and Correlation Analysis ==

==== Topics covered in this section: ====

* Kolmogorov-Smirnov test
* Size of samples, Kolmogorov-Smirnov, Fisher exact
* Logistic regression

=== What forms of evaluation were used to test students’ performance in this section? ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
!align="center"| '''Evaluation'''
!align="center"| '''Yes/No'''
|-
| Development of individual parts of software product code
|align="center"| 0
|-
| Homework and group projects
|align="center"| 0
|-
| Midterm evaluation
|align="center"| 0
|-
| Testing (written or computer based)
|align="center"| 1
|-
| Reports
|align="center"| 0
|-
| Essays
|align="center"| 0
|-
| Oral polls
|align="center"| 0
|-
| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Let X1,X2, ...,X10 be a random sample from a distribution whose probability density function is <math display="inline">f(x) = (1 \quad if \;0 < x < 1</math>, otherwise 0). Based on the observed values 0.62, 0.36, 0.23, 0.76, 0.65, 0.09, 0.55, 0.26, 0.38, 0.24, test the hypothesis H0 : X UNIF(0, 1) against H1 : X UNIF(0, 1) at a significance level = 0.1.
# If X1,X2, ...,Xn is a random sample from a distribution with density function <math display="inline">f(x) = ((1-\theta)x^\theta \; if \; 0 < x < 1</math>, otherwise 0), what is the maximum likelihood estimator of <math display="inline">\theta</math>?
# Let X1,X2, ...,Xn be a random sample of size n from a distribution with a probability density function <math display="inline">f(x) = ((1-\theta)x^\theta \; if \; 0 < x < 1,</math> otherwise 0), where <math display="inline">0 < \theta</math> is a parameter. Using the maximum likelihood method find an estimator for the parameter <math display="inline">\theta</math>.
# Suppose you are told that the likelihood of <math display="inline">\theta</math> at <math display="inline">\theta=2</math> is given by 1/4. Is this the probability that <math display="inline">\theta=2</math>? Explain why or why not.

=== Typical questions for seminar classes (labs) within this section ===

# If X1,X2, ...,Xn is a random sample from a distribution with density function<math display="inline">f(x) = (\frac{1}{\theta} \;if \; 0 < x < 1,</math> otherwise 0), then what is the maximum likelihood estimator of <math display="inline">\theta</math>?
# Let X1,X2, ...,Xn be a random sample from a normal population with mean <math display="inline">\mu</math> and variance <math display="inline">\sigma^2</math>. What are the maximum likelihood estimators of <math display="inline">\mu</math> and <math display="inline">\sigma^2</math>?
# Suppose that you have the following data points: 0.36, 0.32, 0.10, 0.13, 0.45, 0.11, 0.12, 0.09; compute Dn to determine if they come from the uniform distribution [0,0.5].
# The data on the heights of 12 infants are given below: 18.2, 21.4, 22.6, 17.4, 17.6, 16.7, 17.1, 21.4, 20.1, 17.9, 16.8, 23.1. Test the hypothesis that the data came from some normal population at a significance level = 0.1.

=== Test questions for final assessment in the course ===

# Providing full example of two sequences (in case of computational overhead, you can approximate at the first decimal digit). Compute their:
## Covariance.
## Pearson’s correlation coefficient.
## Spearman’s Rank Correlation Coefficient.
## Kendall’s tau Correlation coefficient.
# What is an empirical distribution?
# Present, prove, and discuss the evaluation of the asymptotic confidence interval for the empirical distribution, detailing the role of the binomial.
# Prove, under the simplified hypotheses, the distribution free property of Dn.
# Write the Shannon Theorem and discuss its implications.
# Discuss how we could proceed to compute the confidence interval of the Kendall Tau correlation coefficient of the population.
# Suppose that you have the following datapoints: 0.4, 2, 0.6, 2.4, 2.2, 3.6, 3.8, 4; compute Dn to determine if they come from the uniform distribution [0,4].
# Prove that <math display="inline">\tilde{F}_n</math> is a consistent and unbiased estimator of F.

MSc: Advanced Statistics

2022-04-21T14:42:58Z

R.akhmetzyanov: /* Prerequisites */

= Advanced Statistics =

* '''Course name:''' Advanced Statistics
* '''Course number:''' DS-03

== Prerequisites ==
This course will benefit from good English language skills. Also it could be great if you acquire basic statistical skills in the perspective of [https://en.wikipedia.org/wiki/Empirical_research Empirical research]: statistical hypothesis testing, dependent and independent variables, distributions, etc..
* [https://eduwiki.innopolis.university/index.php/MSc:_Empirical_Methods CSE329 — Empirical Methods]

== Course Characteristics ==
=== Key concepts of the class ===

* Statistical inference
* Non parametric statistics
* Test of statistical hypotheses
* Simple linear regression and correlation analysis
* Meta-Analysis

=== What is the purpose of this course? ===

The main purpose of this course is to present the fundamentals of inferential statistics to the future software engineers and data scientists, on one side providing the scientific fundamentals of the disciplines, and on the other anchoring the theoretical concepts on practices coming from the world of software development and engineering. The course covers the statistical analysis of data with limited assumptions on the distribution, with reference to testing hypotheses, measuring correlations, building samples, and performing regressions.

=== Course objectives based on Bloom’s taxonomy ===

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to:

* Remember the fundamentals of inferential statistics
* Remember the specifics and purpose of different hypothesis tests
* Distinguish between parametric and non parametric tests

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to understand:

* the basic concepts of inferential statistics
* the fundamental laws in statistics
* the concept of null and alternative hypotheses
* the hypotheses test procedure

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to ...

* To understand the problems related to analyse statistically data not distributed normally
* To know the more recent computationally-intensive techniques that can help to describe samples and to infer properties of populations in absence of normality
* To identify situations when the data is on nominal scales so alternative techniques should be use, and act accordingly.
* To be able to run experiment to evaluate hypotheses for situation of scarce data, distributed non normally, on different kinds of scales.

=== Course evaluation (Standard) ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grade breakdown
!
!
!align="center"| '''Points'''
|-
| Weekly quizzes
|
|align="center"| 10
|-
| Midterm
|
|align="center"| 20
|-
| Final oral exam
|
|align="center"| 35
|-
| Final written exam
|
|align="center"| 30
|-
| Participation
|
|align="center"| 5
|}

=== Course evaluation (Project Based) ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grade breakdown
!
!
!align="center"| '''Points'''
|-
| Weekly quizzes
|
|align="center"| 15
|-
| Weekly Projects Review
|
|align="center"| 15
|-
| Mid of Semester Project Review
|
|align="center"| 20
|-
| Final Report
|
|align="center"| 30
|-
| Final Presentation with Q&A
|
|align="center"| 20
|}

=== Grades range ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grading range
!
!
!align="center"| '''Range'''
|-
| A. Excellent
|
|align="center"| 95-100
|-
| B. Good
|
|align="center"| 75-94
|-
| C. Satisfactory
|
|align="center"| 55-74
|-
| D. Poor
|
|align="center"| 0-54
|}

=== Cooperation policy and quotations ===
We encourage vigorous discussion and cooperation in this class. You should feel free to discuss any aspects of the class with any classmates. However, we insist that any written material that is not specifically designated as a Team Deliverable be done by you alone. This includes answers to reading questions, individual reports associated with assignments, and labs. We also insist that if you include verbatim text from any source, you clearly indicate it using standard conventions of quotation or indentation and a note to indicate the source.

=== Resources and reference material ===

* Wasserman L. (2006) All of Nonparametric Statistics. Springer
* Randles, R.H. and Wolfe, D.A. (1991). Introduction to the Theory of Nonparametric Statistics. Melbourne: Robert Krieger. (Ch.1‐Ch.4)
* Hastie, T. Tibshirani, R. and Friedman, J. (2008) The Elements of Statistical Learning 2ed. Springer
* Hollander, M. and Wolfe, D.A. (1999). Nonparametric Statistical Methods, 2nd ed. New York: John Wiley.

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Sampling Distributions Associated with the Normal Population
|align="center"| 15
|-
|align="center"| 2
| Test of Statistical Hypotheses
|align="center"| 30
|-
|align="center"| 3
| Simple Linear Regression and Correlation Analysis
|align="center"| 15
|}

=== Section 1 ===

==== Section title: ====

Sampling Distributions Associated with the Normal Population

=== Topics covered in this section: ===

* Introduction to the course, toward inference
* Student’s t-distribution
* Bernoulli and binomial distribution
* Chi-square distribution
* Snedecor’s F-distribution

=== What forms of evaluation were used to test students’ performance in this section? ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
!align="center"| '''Evaluation'''
!align="center"| '''Yes/No'''
|-
| Development of individual parts of software product code
|align="center"| 0
|-
| Homework and group projects
|align="center"| 0
|-
| Midterm evaluation
|align="center"| 1
|-
| Testing (written or computer based)
|align="center"| 1
|-
| Reports
|align="center"| 0
|-
| Essays
|align="center"| 0
|-
| Oral polls
|align="center"| 0
|-
| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Deduce the probability mass function <math display="inline">P(X \leq k</math> for a binomial distribution?
# Let X1,...,Xk be ''k'' iid random variables distributed with a <math display="inline">\chi^2</math> distribution with n1,...nk degrees of freedom respectively. 
What is the distribution of Y=X1+...+Xk? Define it precisely and prove the answer formally?
# List at least 3 random variables that “tend to follow” a t distribution?
# If X has Chi square function with the 5 degrees of freedom, then what is the probability that X is between 1.145 and 12.83?
# If X has a gamma distribution of (1,1), then what is the probability density function of the random variable 2X?

=== Typical questions for seminar classes (labs) within this section ===

# Define and provide examples of sample space, events and probability measure.
# Write the formula for the coefficients of the simple linear regression. Explain the mathematical procedure you do to derive them and derive them.
# Calculate the correlation between two functions and explain its meaning.
# Calculate the Pearson coefficient for the given functions.
# Deduce the MGF for normal distribution.
# State and prove the Bonferroni inequality.

=== Test questions for final assessment in this section ===

== Test of Statistical Hypotheses ==

=== Topics covered in this section: ===

* Z-test
* Student’s t-test
* Chi-square test
* Snedecor’s F-test

=== What forms of evaluation were used to test students’ performance in this section? ===

=== Typical questions for ongoing performance evaluation within this section ===

# Define the concept of power of a statistical test.
# Define the purpose of the F Test, its hypotheses, and its structure.
# Define the purpose of the t-Test, its hypotheses, and its structure.
# Define the purpose of the Chi square Test, its hypotheses, and its structure.
# Define the purpose of the Z Test, its hypotheses, and its structure.
# Provide concrete numeric examples with explanation on why the power of a test depends on:
## the size of the data sets.
## the magnitude of the effect.
## the level of statistical significance.
# Given a statistical test for which we have set a value <math display="inline">\alpha</math> we obtain a p:
## if we can reject H0 <math display="inline">(p < \alpha)</math>, what we typically say about H0 and H1.
## if we cannot reject H0 <math display="inline">(P \geq \alpha)</math>, what we can typically say about H0 and H1.
## when can we say that H0 holds?
## when can we say that H1 holds?

=== Typical questions for seminar classes (labs) within this section ===

# Provide a concrete example of a t test, detailing both H0 and H1.
# Present the structure of the F test for the analysis of the variance.
# Explain what are H0 and H1 in hypothesis testing.
# Explain the role of the Bonferroni inequality in hypothesis testing.

=== Test questions for final assessment in this section ===

== Simple Linear Regression and Correlation Analysis ==

==== Topics covered in this section: ====

* Kolmogorov-Smirnov test
* Size of samples, Kolmogorov-Smirnov, Fisher exact
* Logistic regression

=== What forms of evaluation were used to test students’ performance in this section? ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
!align="center"| '''Evaluation'''
!align="center"| '''Yes/No'''
|-
| Development of individual parts of software product code
|align="center"| 0
|-
| Homework and group projects
|align="center"| 0
|-
| Midterm evaluation
|align="center"| 0
|-
| Testing (written or computer based)
|align="center"| 1
|-
| Reports
|align="center"| 0
|-
| Essays
|align="center"| 0
|-
| Oral polls
|align="center"| 0
|-
| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Let X1,X2, ...,X10 be a random sample from a distribution whose probability density function is <math display="inline">f(x) = (1 \quad if \;0 < x < 1</math>, otherwise 0). Based on the observed values 0.62, 0.36, 0.23, 0.76, 0.65, 0.09, 0.55, 0.26, 0.38, 0.24, test the hypothesis H0 : X UNIF(0, 1) against H1 : X UNIF(0, 1) at a significance level = 0.1.
# If X1,X2, ...,Xn is a random sample from a distribution with density function <math display="inline">f(x) = ((1-\theta)x^\theta \; if \; 0 < x < 1</math>, otherwise 0), what is the maximum likelihood estimator of <math display="inline">\theta</math>?
# Let X1,X2, ...,Xn be a random sample of size n from a distribution with a probability density function <math display="inline">f(x) = ((1-\theta)x^\theta \; if \; 0 < x < 1,</math> otherwise 0), where <math display="inline">0 < \theta</math> is a parameter. Using the maximum likelihood method find an estimator for the parameter <math display="inline">\theta</math>.
# Suppose you are told that the likelihood of <math display="inline">\theta</math> at <math display="inline">\theta=2</math> is given by 1/4. Is this the probability that <math display="inline">\theta=2</math>? Explain why or why not.

=== Typical questions for seminar classes (labs) within this section ===

# If X1,X2, ...,Xn is a random sample from a distribution with density function<math display="inline">f(x) = (\frac{1}{\theta} \;if \; 0 < x < 1,</math> otherwise 0), then what is the maximum likelihood estimator of <math display="inline">\theta</math>?
# Let X1,X2, ...,Xn be a random sample from a normal population with mean <math display="inline">\mu</math> and variance <math display="inline">\sigma^2</math>. What are the maximum likelihood estimators of <math display="inline">\mu</math> and <math display="inline">\sigma^2</math>?
# Suppose that you have the following data points: 0.36, 0.32, 0.10, 0.13, 0.45, 0.11, 0.12, 0.09; compute Dn to determine if they come from the uniform distribution [0,0.5].
# The data on the heights of 12 infants are given below: 18.2, 21.4, 22.6, 17.4, 17.6, 16.7, 17.1, 21.4, 20.1, 17.9, 16.8, 23.1. Test the hypothesis that the data came from some normal population at a significance level = 0.1.

=== Test questions for final assessment in the course ===

# Providing full example of two sequences (in case of computational overhead, you can approximate at the first decimal digit). Compute their:
## Covariance.
## Pearson’s correlation coefficient.
## Spearman’s Rank Correlation Coefficient.
## Kendall’s tau Correlation coefficient.
# What is an empirical distribution?
# Present, prove, and discuss the evaluation of the asymptotic confidence interval for the empirical distribution, detailing the role of the binomial.
# Prove, under the simplified hypotheses, the distribution free property of Dn.
# Write the Shannon Theorem and discuss its implications.
# Discuss how we could proceed to compute the confidence interval of the Kendall Tau correlation coefficient of the population.
# Suppose that you have the following datapoints: 0.4, 2, 0.6, 2.4, 2.2, 3.6, 3.8, 4; compute Dn to determine if they come from the uniform distribution [0,4].
# Prove that <math display="inline">\tilde{F}_n</math> is a consistent and unbiased estimator of F.

MSc: Advanced Statistics

2022-04-21T14:32:52Z

R.akhmetzyanov: /* Prerequisites */

= Advanced Statistics =

* '''Course name:''' Advanced Statistics
* '''Course number:''' DS-03

== Prerequisites ==
This course will benefit from good English language skills. Also it could be great if you acquire basic statistical skills in the perspective of [https://en.wikipedia.org/wiki/Empirical_research Empirical research]: statistical hypothesis testing, dependent and independent variables, distributions, etc..
* [https://eduwiki.innopolis.university/index.php/MSc:_Empirical_Methods CSE329 — Empirical Methods]

== Course Characteristics ==
=== Key concepts of the class ===

* Statistical inference
* Non parametric statistics
* Test of statistical hypotheses
* Simple linear regression and correlation analysis
* Meta-Analysis

=== What is the purpose of this course? ===

The main purpose of this course is to present the fundamentals of inferential statistics to the future software engineers and data scientists, on one side providing the scientific fundamentals of the disciplines, and on the other anchoring the theoretical concepts on practices coming from the world of software development and engineering. The course covers the statistical analysis of data with limited assumptions on the distribution, with reference to testing hypotheses, measuring correlations, building samples, and performing regressions.

=== Course objectives based on Bloom’s taxonomy ===

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to:

* Remember the fundamentals of inferential statistics
* Remember the specifics and purpose of different hypothesis tests
* Distinguish between parametric and non parametric tests

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to understand:

* the basic concepts of inferential statistics
* the fundamental laws in statistics
* the concept of null and alternative hypotheses
* the hypotheses test procedure

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to ...

* To understand the problems related to analyse statistically data not distributed normally
* To know the more recent computationally-intensive techniques that can help to describe samples and to infer properties of populations in absence of normality
* To identify situations when the data is on nominal scales so alternative techniques should be use, and act accordingly.
* To be able to run experiment to evaluate hypotheses for situation of scarce data, distributed non normally, on different kinds of scales.

=== Course evaluation (Standard) ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grade breakdown
!
!
!align="center"| '''Points'''
|-
| Weekly quizzes
|
|align="center"| 10
|-
| Midterm
|
|align="center"| 20
|-
| Final oral exam
|
|align="center"| 35
|-
| Final written exam
|
|align="center"| 30
|-
| Participation
|
|align="center"| 5
|}

=== Course evaluation (Project Based) ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grade breakdown
!
!
!align="center"| '''Points'''
|-
| Weekly quizzes
|
|align="center"| 15
|-
| Weekly Projects Review
|
|align="center"| 15
|-
| Mid of Semester Project Review
|
|align="center"| 20
|-
| Final Report
|
|align="center"| 30
|-
| Final Presentation with Q&A
|
|align="center"| 20
|}

=== Grades range ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grading range
!
!
!align="center"| '''Range'''
|-
| A. Excellent
|
|align="center"| 95-100
|-
| B. Good
|
|align="center"| 75-94
|-
| C. Satisfactory
|
|align="center"| 55-74
|-
| D. Poor
|
|align="center"| 0-54
|}

=== Cooperation policy and quotations ===
We encourage vigorous discussion and cooperation in this class. You should feel free to discuss any aspects of the class with any classmates. However, we insist that any written material that is not specifically designated as a Team Deliverable be done by you alone. This includes answers to reading questions, individual reports associated with assignments, and labs. We also insist that if you include verbatim text from any source, you clearly indicate it using standard conventions of quotation or indentation and a note to indicate the source.

=== Resources and reference material ===

* Wasserman L. (2006) All of Nonparametric Statistics. Springer
* Randles, R.H. and Wolfe, D.A. (1991). Introduction to the Theory of Nonparametric Statistics. Melbourne: Robert Krieger. (Ch.1‐Ch.4)
* Hastie, T. Tibshirani, R. and Friedman, J. (2008) The Elements of Statistical Learning 2ed. Springer
* Hollander, M. and Wolfe, D.A. (1999). Nonparametric Statistical Methods, 2nd ed. New York: John Wiley.

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Sampling Distributions Associated with the Normal Population
|align="center"| 15
|-
|align="center"| 2
| Test of Statistical Hypotheses
|align="center"| 30
|-
|align="center"| 3
| Simple Linear Regression and Correlation Analysis
|align="center"| 15
|}

=== Section 1 ===

==== Section title: ====

Sampling Distributions Associated with the Normal Population

=== Topics covered in this section: ===

* Introduction to the course, toward inference
* Student’s t-distribution
* Bernoulli and binomial distribution
* Chi-square distribution
* Snedecor’s F-distribution

=== What forms of evaluation were used to test students’ performance in this section? ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
!align="center"| '''Evaluation'''
!align="center"| '''Yes/No'''
|-
| Development of individual parts of software product code
|align="center"| 0
|-
| Homework and group projects
|align="center"| 0
|-
| Midterm evaluation
|align="center"| 1
|-
| Testing (written or computer based)
|align="center"| 1
|-
| Reports
|align="center"| 0
|-
| Essays
|align="center"| 0
|-
| Oral polls
|align="center"| 0
|-
| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Deduce the probability mass function <math display="inline">P(X \leq k</math> for a binomial distribution?
# Let X1,...,Xk be ''k'' iid random variables distributed with a <math display="inline">\chi^2</math> distribution with n1,...nk degrees of freedom respectively. 
What is the distribution of Y=X1+...+Xk? Define it precisely and prove the answer formally?
# List at least 3 random variables that “tend to follow” a t distribution?
# If X has Chi square function with the 5 degrees of freedom, then what is the probability that X is between 1.145 and 12.83?
# If X has a gamma distribution of (1,1), then what is the probability density function of the random variable 2X?

=== Typical questions for seminar classes (labs) within this section ===

# Define and provide examples of sample space, events and probability measure.
# Write the formula for the coefficients of the simple linear regression. Explain the mathematical procedure you do to derive them and derive them.
# Calculate the correlation between two functions and explain its meaning.
# Calculate the Pearson coefficient for the given functions.
# Deduce the MGF for normal distribution.
# State and prove the Bonferroni inequality.

=== Test questions for final assessment in this section ===

== Test of Statistical Hypotheses ==

=== Topics covered in this section: ===

* Z-test
* Student’s t-test
* Chi-square test
* Snedecor’s F-test

=== What forms of evaluation were used to test students’ performance in this section? ===

=== Typical questions for ongoing performance evaluation within this section ===

# Define the concept of power of a statistical test.
# Define the purpose of the F Test, its hypotheses, and its structure.
# Define the purpose of the t-Test, its hypotheses, and its structure.
# Define the purpose of the Chi square Test, its hypotheses, and its structure.
# Define the purpose of the Z Test, its hypotheses, and its structure.
# Provide concrete numeric examples with explanation on why the power of a test depends on:
## the size of the data sets.
## the magnitude of the effect.
## the level of statistical significance.
# Given a statistical test for which we have set a value <math display="inline">\alpha</math> we obtain a p:
## if we can reject H0 <math display="inline">(p < \alpha)</math>, what we typically say about H0 and H1.
## if we cannot reject H0 <math display="inline">(P \geq \alpha)</math>, what we can typically say about H0 and H1.
## when can we say that H0 holds?
## when can we say that H1 holds?

=== Typical questions for seminar classes (labs) within this section ===

# Provide a concrete example of a t test, detailing both H0 and H1.
# Present the structure of the F test for the analysis of the variance.
# Explain what are H0 and H1 in hypothesis testing.
# Explain the role of the Bonferroni inequality in hypothesis testing.

=== Test questions for final assessment in this section ===

== Simple Linear Regression and Correlation Analysis ==

==== Topics covered in this section: ====

* Kolmogorov-Smirnov test
* Size of samples, Kolmogorov-Smirnov, Fisher exact
* Logistic regression

=== What forms of evaluation were used to test students’ performance in this section? ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
!align="center"| '''Evaluation'''
!align="center"| '''Yes/No'''
|-
| Development of individual parts of software product code
|align="center"| 0
|-
| Homework and group projects
|align="center"| 0
|-
| Midterm evaluation
|align="center"| 0
|-
| Testing (written or computer based)
|align="center"| 1
|-
| Reports
|align="center"| 0
|-
| Essays
|align="center"| 0
|-
| Oral polls
|align="center"| 0
|-
| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Let X1,X2, ...,X10 be a random sample from a distribution whose probability density function is <math display="inline">f(x) = (1 \quad if \;0 < x < 1</math>, otherwise 0). Based on the observed values 0.62, 0.36, 0.23, 0.76, 0.65, 0.09, 0.55, 0.26, 0.38, 0.24, test the hypothesis H0 : X UNIF(0, 1) against H1 : X UNIF(0, 1) at a significance level = 0.1.
# If X1,X2, ...,Xn is a random sample from a distribution with density function <math display="inline">f(x) = ((1-\theta)x^\theta \; if \; 0 < x < 1</math>, otherwise 0), what is the maximum likelihood estimator of <math display="inline">\theta</math>?
# Let X1,X2, ...,Xn be a random sample of size n from a distribution with a probability density function <math display="inline">f(x) = ((1-\theta)x^\theta \; if \; 0 < x < 1,</math> otherwise 0), where <math display="inline">0 < \theta</math> is a parameter. Using the maximum likelihood method find an estimator for the parameter <math display="inline">\theta</math>.
# Suppose you are told that the likelihood of <math display="inline">\theta</math> at <math display="inline">\theta=2</math> is given by 1/4. Is this the probability that <math display="inline">\theta=2</math>? Explain why or why not.

=== Typical questions for seminar classes (labs) within this section ===

# If X1,X2, ...,Xn is a random sample from a distribution with density function<math display="inline">f(x) = (\frac{1}{\theta} \;if \; 0 < x < 1,</math> otherwise 0), then what is the maximum likelihood estimator of <math display="inline">\theta</math>?
# Let X1,X2, ...,Xn be a random sample from a normal population with mean <math display="inline">\mu</math> and variance <math display="inline">\sigma^2</math>. What are the maximum likelihood estimators of <math display="inline">\mu</math> and <math display="inline">\sigma^2</math>?
# Suppose that you have the following data points: 0.36, 0.32, 0.10, 0.13, 0.45, 0.11, 0.12, 0.09; compute Dn to determine if they come from the uniform distribution [0,0.5].
# The data on the heights of 12 infants are given below: 18.2, 21.4, 22.6, 17.4, 17.6, 16.7, 17.1, 21.4, 20.1, 17.9, 16.8, 23.1. Test the hypothesis that the data came from some normal population at a significance level = 0.1.

=== Test questions for final assessment in the course ===

# Providing full example of two sequences (in case of computational overhead, you can approximate at the first decimal digit). Compute their:
## Covariance.
## Pearson’s correlation coefficient.
## Spearman’s Rank Correlation Coefficient.
## Kendall’s tau Correlation coefficient.
# What is an empirical distribution?
# Present, prove, and discuss the evaluation of the asymptotic confidence interval for the empirical distribution, detailing the role of the binomial.
# Prove, under the simplified hypotheses, the distribution free property of Dn.
# Write the Shannon Theorem and discuss its implications.
# Discuss how we could proceed to compute the confidence interval of the Kendall Tau correlation coefficient of the population.
# Suppose that you have the following datapoints: 0.4, 2, 0.6, 2.4, 2.2, 3.6, 3.8, 4; compute Dn to determine if they come from the uniform distribution [0,4].
# Prove that <math display="inline">\tilde{F}_n</math> is a consistent and unbiased estimator of F.

MSc: Advanced Statistics

2022-04-21T14:32:07Z

R.akhmetzyanov: /* Prerequisites */

= Advanced Statistics =

* '''Course name:''' Advanced Statistics
* '''Course number:''' DS-03

== Prerequisites ==
This course will benefit from good English language skills. Also it could be great if you acquire basic statistical skills in the perspective of [https://en.wikipedia.org/wiki/Empirical_research Empirical research]: statistical hypothesis testing, dependent and independent variables, distributions, etc..
* [https://eduwiki.innopolis.university/index.php/MSc:_Empirical_Methods CSE329 — Empirical Methods]
* [https://eduwiki.innopolis.university/index.php/MSc:_Classical_Internet_Applications CSE517 - Classical Internet Applications]

== Course Characteristics ==
=== Key concepts of the class ===

* Statistical inference
* Non parametric statistics
* Test of statistical hypotheses
* Simple linear regression and correlation analysis
* Meta-Analysis

=== What is the purpose of this course? ===

The main purpose of this course is to present the fundamentals of inferential statistics to the future software engineers and data scientists, on one side providing the scientific fundamentals of the disciplines, and on the other anchoring the theoretical concepts on practices coming from the world of software development and engineering. The course covers the statistical analysis of data with limited assumptions on the distribution, with reference to testing hypotheses, measuring correlations, building samples, and performing regressions.

=== Course objectives based on Bloom’s taxonomy ===

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to:

* Remember the fundamentals of inferential statistics
* Remember the specifics and purpose of different hypothesis tests
* Distinguish between parametric and non parametric tests

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to understand:

* the basic concepts of inferential statistics
* the fundamental laws in statistics
* the concept of null and alternative hypotheses
* the hypotheses test procedure

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to ...

* To understand the problems related to analyse statistically data not distributed normally
* To know the more recent computationally-intensive techniques that can help to describe samples and to infer properties of populations in absence of normality
* To identify situations when the data is on nominal scales so alternative techniques should be use, and act accordingly.
* To be able to run experiment to evaluate hypotheses for situation of scarce data, distributed non normally, on different kinds of scales.

=== Course evaluation (Standard) ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grade breakdown
!
!
!align="center"| '''Points'''
|-
| Weekly quizzes
|
|align="center"| 10
|-
| Midterm
|
|align="center"| 20
|-
| Final oral exam
|
|align="center"| 35
|-
| Final written exam
|
|align="center"| 30
|-
| Participation
|
|align="center"| 5
|}

=== Course evaluation (Project Based) ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grade breakdown
!
!
!align="center"| '''Points'''
|-
| Weekly quizzes
|
|align="center"| 15
|-
| Weekly Projects Review
|
|align="center"| 15
|-
| Mid of Semester Project Review
|
|align="center"| 20
|-
| Final Report
|
|align="center"| 30
|-
| Final Presentation with Q&A
|
|align="center"| 20
|}

=== Grades range ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grading range
!
!
!align="center"| '''Range'''
|-
| A. Excellent
|
|align="center"| 95-100
|-
| B. Good
|
|align="center"| 75-94
|-
| C. Satisfactory
|
|align="center"| 55-74
|-
| D. Poor
|
|align="center"| 0-54
|}

=== Cooperation policy and quotations ===
We encourage vigorous discussion and cooperation in this class. You should feel free to discuss any aspects of the class with any classmates. However, we insist that any written material that is not specifically designated as a Team Deliverable be done by you alone. This includes answers to reading questions, individual reports associated with assignments, and labs. We also insist that if you include verbatim text from any source, you clearly indicate it using standard conventions of quotation or indentation and a note to indicate the source.

=== Resources and reference material ===

* Wasserman L. (2006) All of Nonparametric Statistics. Springer
* Randles, R.H. and Wolfe, D.A. (1991). Introduction to the Theory of Nonparametric Statistics. Melbourne: Robert Krieger. (Ch.1‐Ch.4)
* Hastie, T. Tibshirani, R. and Friedman, J. (2008) The Elements of Statistical Learning 2ed. Springer
* Hollander, M. and Wolfe, D.A. (1999). Nonparametric Statistical Methods, 2nd ed. New York: John Wiley.

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Sampling Distributions Associated with the Normal Population
|align="center"| 15
|-
|align="center"| 2
| Test of Statistical Hypotheses
|align="center"| 30
|-
|align="center"| 3
| Simple Linear Regression and Correlation Analysis
|align="center"| 15
|}

=== Section 1 ===

==== Section title: ====

Sampling Distributions Associated with the Normal Population

=== Topics covered in this section: ===

* Introduction to the course, toward inference
* Student’s t-distribution
* Bernoulli and binomial distribution
* Chi-square distribution
* Snedecor’s F-distribution

=== What forms of evaluation were used to test students’ performance in this section? ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
!align="center"| '''Evaluation'''
!align="center"| '''Yes/No'''
|-
| Development of individual parts of software product code
|align="center"| 0
|-
| Homework and group projects
|align="center"| 0
|-
| Midterm evaluation
|align="center"| 1
|-
| Testing (written or computer based)
|align="center"| 1
|-
| Reports
|align="center"| 0
|-
| Essays
|align="center"| 0
|-
| Oral polls
|align="center"| 0
|-
| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Deduce the probability mass function <math display="inline">P(X \leq k</math> for a binomial distribution?
# Let X1,...,Xk be ''k'' iid random variables distributed with a <math display="inline">\chi^2</math> distribution with n1,...nk degrees of freedom respectively. 
What is the distribution of Y=X1+...+Xk? Define it precisely and prove the answer formally?
# List at least 3 random variables that “tend to follow” a t distribution?
# If X has Chi square function with the 5 degrees of freedom, then what is the probability that X is between 1.145 and 12.83?
# If X has a gamma distribution of (1,1), then what is the probability density function of the random variable 2X?

=== Typical questions for seminar classes (labs) within this section ===

# Define and provide examples of sample space, events and probability measure.
# Write the formula for the coefficients of the simple linear regression. Explain the mathematical procedure you do to derive them and derive them.
# Calculate the correlation between two functions and explain its meaning.
# Calculate the Pearson coefficient for the given functions.
# Deduce the MGF for normal distribution.
# State and prove the Bonferroni inequality.

=== Test questions for final assessment in this section ===

== Test of Statistical Hypotheses ==

=== Topics covered in this section: ===

* Z-test
* Student’s t-test
* Chi-square test
* Snedecor’s F-test

=== What forms of evaluation were used to test students’ performance in this section? ===

=== Typical questions for ongoing performance evaluation within this section ===

# Define the concept of power of a statistical test.
# Define the purpose of the F Test, its hypotheses, and its structure.
# Define the purpose of the t-Test, its hypotheses, and its structure.
# Define the purpose of the Chi square Test, its hypotheses, and its structure.
# Define the purpose of the Z Test, its hypotheses, and its structure.
# Provide concrete numeric examples with explanation on why the power of a test depends on:
## the size of the data sets.
## the magnitude of the effect.
## the level of statistical significance.
# Given a statistical test for which we have set a value <math display="inline">\alpha</math> we obtain a p:
## if we can reject H0 <math display="inline">(p < \alpha)</math>, what we typically say about H0 and H1.
## if we cannot reject H0 <math display="inline">(P \geq \alpha)</math>, what we can typically say about H0 and H1.
## when can we say that H0 holds?
## when can we say that H1 holds?

=== Typical questions for seminar classes (labs) within this section ===

# Provide a concrete example of a t test, detailing both H0 and H1.
# Present the structure of the F test for the analysis of the variance.
# Explain what are H0 and H1 in hypothesis testing.
# Explain the role of the Bonferroni inequality in hypothesis testing.

=== Test questions for final assessment in this section ===

== Simple Linear Regression and Correlation Analysis ==

==== Topics covered in this section: ====

* Kolmogorov-Smirnov test
* Size of samples, Kolmogorov-Smirnov, Fisher exact
* Logistic regression

=== What forms of evaluation were used to test students’ performance in this section? ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
!align="center"| '''Evaluation'''
!align="center"| '''Yes/No'''
|-
| Development of individual parts of software product code
|align="center"| 0
|-
| Homework and group projects
|align="center"| 0
|-
| Midterm evaluation
|align="center"| 0
|-
| Testing (written or computer based)
|align="center"| 1
|-
| Reports
|align="center"| 0
|-
| Essays
|align="center"| 0
|-
| Oral polls
|align="center"| 0
|-
| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Let X1,X2, ...,X10 be a random sample from a distribution whose probability density function is <math display="inline">f(x) = (1 \quad if \;0 < x < 1</math>, otherwise 0). Based on the observed values 0.62, 0.36, 0.23, 0.76, 0.65, 0.09, 0.55, 0.26, 0.38, 0.24, test the hypothesis H0 : X UNIF(0, 1) against H1 : X UNIF(0, 1) at a significance level = 0.1.
# If X1,X2, ...,Xn is a random sample from a distribution with density function <math display="inline">f(x) = ((1-\theta)x^\theta \; if \; 0 < x < 1</math>, otherwise 0), what is the maximum likelihood estimator of <math display="inline">\theta</math>?
# Let X1,X2, ...,Xn be a random sample of size n from a distribution with a probability density function <math display="inline">f(x) = ((1-\theta)x^\theta \; if \; 0 < x < 1,</math> otherwise 0), where <math display="inline">0 < \theta</math> is a parameter. Using the maximum likelihood method find an estimator for the parameter <math display="inline">\theta</math>.
# Suppose you are told that the likelihood of <math display="inline">\theta</math> at <math display="inline">\theta=2</math> is given by 1/4. Is this the probability that <math display="inline">\theta=2</math>? Explain why or why not.

=== Typical questions for seminar classes (labs) within this section ===

# If X1,X2, ...,Xn is a random sample from a distribution with density function<math display="inline">f(x) = (\frac{1}{\theta} \;if \; 0 < x < 1,</math> otherwise 0), then what is the maximum likelihood estimator of <math display="inline">\theta</math>?
# Let X1,X2, ...,Xn be a random sample from a normal population with mean <math display="inline">\mu</math> and variance <math display="inline">\sigma^2</math>. What are the maximum likelihood estimators of <math display="inline">\mu</math> and <math display="inline">\sigma^2</math>?
# Suppose that you have the following data points: 0.36, 0.32, 0.10, 0.13, 0.45, 0.11, 0.12, 0.09; compute Dn to determine if they come from the uniform distribution [0,0.5].
# The data on the heights of 12 infants are given below: 18.2, 21.4, 22.6, 17.4, 17.6, 16.7, 17.1, 21.4, 20.1, 17.9, 16.8, 23.1. Test the hypothesis that the data came from some normal population at a significance level = 0.1.

=== Test questions for final assessment in the course ===

# Providing full example of two sequences (in case of computational overhead, you can approximate at the first decimal digit). Compute their:
## Covariance.
## Pearson’s correlation coefficient.
## Spearman’s Rank Correlation Coefficient.
## Kendall’s tau Correlation coefficient.
# What is an empirical distribution?
# Present, prove, and discuss the evaluation of the asymptotic confidence interval for the empirical distribution, detailing the role of the binomial.
# Prove, under the simplified hypotheses, the distribution free property of Dn.
# Write the Shannon Theorem and discuss its implications.
# Discuss how we could proceed to compute the confidence interval of the Kendall Tau correlation coefficient of the population.
# Suppose that you have the following datapoints: 0.4, 2, 0.6, 2.4, 2.2, 3.6, 3.8, 4; compute Dn to determine if they come from the uniform distribution [0,4].
# Prove that <math display="inline">\tilde{F}_n</math> is a consistent and unbiased estimator of F.

MSc: Large Systems

2022-04-21T14:30:51Z

R.akhmetzyanov: /* Prerequisites */

== Prerequisites ==
The course has been designed to be self-included as much as possible. The successful completion will depend on prerequisite courses such as
* [https://eduwiki.innopolis.university/index.php/MSc:_Classical_Internet_Applications CSE517 - Classical Internet Applications]
* [https://eduwiki.innopolis.university/index.php/MSc:_Inter_Networking_and_Routing CSE518 - Internetworking And Routing]
* Essential skills

MSc: Large Systems

2022-04-21T14:30:41Z

R.akhmetzyanov: /* Prerequisites */

== Prerequisites ==
text
The course has been designed to be self-included as much as possible. The successful completion will depend on prerequisite courses such as
* [https://eduwiki.innopolis.university/index.php/MSc:_Classical_Internet_Applications CSE517 - Classical Internet Applications]
* [https://eduwiki.innopolis.university/index.php/MSc:_Inter_Networking_and_Routing CSE518 - Internetworking And Routing]
* Essential skills

MSc: Advanced Statistics

2022-04-21T14:29:13Z

R.akhmetzyanov: /* Prerequisites */

= Advanced Statistics =

* '''Course name:''' Advanced Statistics
* '''Course number:''' DS-03

== Prerequisites ==

This course will benefit from good English language skills. Also it could be great if you acquire basic statistical skills in the perspective of [https://en.wikipedia.org/wiki/Empirical_research Empirical research]: statistical hypothesis testing, dependent and independent variables, distributions, etc..
* [https://eduwiki.innopolis.university/index.php/MSc:_Empirical_Methods CSE329 — Empirical Methods]

== Course Characteristics ==
=== Key concepts of the class ===

* Statistical inference
* Non parametric statistics
* Test of statistical hypotheses
* Simple linear regression and correlation analysis
* Meta-Analysis

=== What is the purpose of this course? ===

The main purpose of this course is to present the fundamentals of inferential statistics to the future software engineers and data scientists, on one side providing the scientific fundamentals of the disciplines, and on the other anchoring the theoretical concepts on practices coming from the world of software development and engineering. The course covers the statistical analysis of data with limited assumptions on the distribution, with reference to testing hypotheses, measuring correlations, building samples, and performing regressions.

=== Course objectives based on Bloom’s taxonomy ===

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to:

* Remember the fundamentals of inferential statistics
* Remember the specifics and purpose of different hypothesis tests
* Distinguish between parametric and non parametric tests

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to understand:

* the basic concepts of inferential statistics
* the fundamental laws in statistics
* the concept of null and alternative hypotheses
* the hypotheses test procedure

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to ...

* To understand the problems related to analyse statistically data not distributed normally
* To know the more recent computationally-intensive techniques that can help to describe samples and to infer properties of populations in absence of normality
* To identify situations when the data is on nominal scales so alternative techniques should be use, and act accordingly.
* To be able to run experiment to evaluate hypotheses for situation of scarce data, distributed non normally, on different kinds of scales.

=== Course evaluation (Standard) ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grade breakdown
!
!
!align="center"| '''Points'''
|-
| Weekly quizzes
|
|align="center"| 10
|-
| Midterm
|
|align="center"| 20
|-
| Final oral exam
|
|align="center"| 35
|-
| Final written exam
|
|align="center"| 30
|-
| Participation
|
|align="center"| 5
|}

=== Course evaluation (Project Based) ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grade breakdown
!
!
!align="center"| '''Points'''
|-
| Weekly quizzes
|
|align="center"| 15
|-
| Weekly Projects Review
|
|align="center"| 15
|-
| Mid of Semester Project Review
|
|align="center"| 20
|-
| Final Report
|
|align="center"| 30
|-
| Final Presentation with Q&A
|
|align="center"| 20
|}

=== Grades range ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grading range
!
!
!align="center"| '''Range'''
|-
| A. Excellent
|
|align="center"| 95-100
|-
| B. Good
|
|align="center"| 75-94
|-
| C. Satisfactory
|
|align="center"| 55-74
|-
| D. Poor
|
|align="center"| 0-54
|}

=== Cooperation policy and quotations ===
We encourage vigorous discussion and cooperation in this class. You should feel free to discuss any aspects of the class with any classmates. However, we insist that any written material that is not specifically designated as a Team Deliverable be done by you alone. This includes answers to reading questions, individual reports associated with assignments, and labs. We also insist that if you include verbatim text from any source, you clearly indicate it using standard conventions of quotation or indentation and a note to indicate the source.

=== Resources and reference material ===

* Wasserman L. (2006) All of Nonparametric Statistics. Springer
* Randles, R.H. and Wolfe, D.A. (1991). Introduction to the Theory of Nonparametric Statistics. Melbourne: Robert Krieger. (Ch.1‐Ch.4)
* Hastie, T. Tibshirani, R. and Friedman, J. (2008) The Elements of Statistical Learning 2ed. Springer
* Hollander, M. and Wolfe, D.A. (1999). Nonparametric Statistical Methods, 2nd ed. New York: John Wiley.

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Sampling Distributions Associated with the Normal Population
|align="center"| 15
|-
|align="center"| 2
| Test of Statistical Hypotheses
|align="center"| 30
|-
|align="center"| 3
| Simple Linear Regression and Correlation Analysis
|align="center"| 15
|}

=== Section 1 ===

==== Section title: ====

Sampling Distributions Associated with the Normal Population

=== Topics covered in this section: ===

* Introduction to the course, toward inference
* Student’s t-distribution
* Bernoulli and binomial distribution
* Chi-square distribution
* Snedecor’s F-distribution

=== What forms of evaluation were used to test students’ performance in this section? ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
!align="center"| '''Evaluation'''
!align="center"| '''Yes/No'''
|-
| Development of individual parts of software product code
|align="center"| 0
|-
| Homework and group projects
|align="center"| 0
|-
| Midterm evaluation
|align="center"| 1
|-
| Testing (written or computer based)
|align="center"| 1
|-
| Reports
|align="center"| 0
|-
| Essays
|align="center"| 0
|-
| Oral polls
|align="center"| 0
|-
| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Deduce the probability mass function <math display="inline">P(X \leq k</math> for a binomial distribution?
# Let X1,...,Xk be ''k'' iid random variables distributed with a <math display="inline">\chi^2</math> distribution with n1,...nk degrees of freedom respectively. 
What is the distribution of Y=X1+...+Xk? Define it precisely and prove the answer formally?
# List at least 3 random variables that “tend to follow” a t distribution?
# If X has Chi square function with the 5 degrees of freedom, then what is the probability that X is between 1.145 and 12.83?
# If X has a gamma distribution of (1,1), then what is the probability density function of the random variable 2X?

=== Typical questions for seminar classes (labs) within this section ===

# Define and provide examples of sample space, events and probability measure.
# Write the formula for the coefficients of the simple linear regression. Explain the mathematical procedure you do to derive them and derive them.
# Calculate the correlation between two functions and explain its meaning.
# Calculate the Pearson coefficient for the given functions.
# Deduce the MGF for normal distribution.
# State and prove the Bonferroni inequality.

=== Test questions for final assessment in this section ===

== Test of Statistical Hypotheses ==

=== Topics covered in this section: ===

* Z-test
* Student’s t-test
* Chi-square test
* Snedecor’s F-test

=== What forms of evaluation were used to test students’ performance in this section? ===

=== Typical questions for ongoing performance evaluation within this section ===

# Define the concept of power of a statistical test.
# Define the purpose of the F Test, its hypotheses, and its structure.
# Define the purpose of the t-Test, its hypotheses, and its structure.
# Define the purpose of the Chi square Test, its hypotheses, and its structure.
# Define the purpose of the Z Test, its hypotheses, and its structure.
# Provide concrete numeric examples with explanation on why the power of a test depends on:
## the size of the data sets.
## the magnitude of the effect.
## the level of statistical significance.
# Given a statistical test for which we have set a value <math display="inline">\alpha</math> we obtain a p:
## if we can reject H0 <math display="inline">(p < \alpha)</math>, what we typically say about H0 and H1.
## if we cannot reject H0 <math display="inline">(P \geq \alpha)</math>, what we can typically say about H0 and H1.
## when can we say that H0 holds?
## when can we say that H1 holds?

=== Typical questions for seminar classes (labs) within this section ===

# Provide a concrete example of a t test, detailing both H0 and H1.
# Present the structure of the F test for the analysis of the variance.
# Explain what are H0 and H1 in hypothesis testing.
# Explain the role of the Bonferroni inequality in hypothesis testing.

=== Test questions for final assessment in this section ===

== Simple Linear Regression and Correlation Analysis ==

==== Topics covered in this section: ====

* Kolmogorov-Smirnov test
* Size of samples, Kolmogorov-Smirnov, Fisher exact
* Logistic regression

=== What forms of evaluation were used to test students’ performance in this section? ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
!align="center"| '''Evaluation'''
!align="center"| '''Yes/No'''
|-
| Development of individual parts of software product code
|align="center"| 0
|-
| Homework and group projects
|align="center"| 0
|-
| Midterm evaluation
|align="center"| 0
|-
| Testing (written or computer based)
|align="center"| 1
|-
| Reports
|align="center"| 0
|-
| Essays
|align="center"| 0
|-
| Oral polls
|align="center"| 0
|-
| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Let X1,X2, ...,X10 be a random sample from a distribution whose probability density function is <math display="inline">f(x) = (1 \quad if \;0 < x < 1</math>, otherwise 0). Based on the observed values 0.62, 0.36, 0.23, 0.76, 0.65, 0.09, 0.55, 0.26, 0.38, 0.24, test the hypothesis H0 : X UNIF(0, 1) against H1 : X UNIF(0, 1) at a significance level = 0.1.
# If X1,X2, ...,Xn is a random sample from a distribution with density function <math display="inline">f(x) = ((1-\theta)x^\theta \; if \; 0 < x < 1</math>, otherwise 0), what is the maximum likelihood estimator of <math display="inline">\theta</math>?
# Let X1,X2, ...,Xn be a random sample of size n from a distribution with a probability density function <math display="inline">f(x) = ((1-\theta)x^\theta \; if \; 0 < x < 1,</math> otherwise 0), where <math display="inline">0 < \theta</math> is a parameter. Using the maximum likelihood method find an estimator for the parameter <math display="inline">\theta</math>.
# Suppose you are told that the likelihood of <math display="inline">\theta</math> at <math display="inline">\theta=2</math> is given by 1/4. Is this the probability that <math display="inline">\theta=2</math>? Explain why or why not.

=== Typical questions for seminar classes (labs) within this section ===

# If X1,X2, ...,Xn is a random sample from a distribution with density function<math display="inline">f(x) = (\frac{1}{\theta} \;if \; 0 < x < 1,</math> otherwise 0), then what is the maximum likelihood estimator of <math display="inline">\theta</math>?
# Let X1,X2, ...,Xn be a random sample from a normal population with mean <math display="inline">\mu</math> and variance <math display="inline">\sigma^2</math>. What are the maximum likelihood estimators of <math display="inline">\mu</math> and <math display="inline">\sigma^2</math>?
# Suppose that you have the following data points: 0.36, 0.32, 0.10, 0.13, 0.45, 0.11, 0.12, 0.09; compute Dn to determine if they come from the uniform distribution [0,0.5].
# The data on the heights of 12 infants are given below: 18.2, 21.4, 22.6, 17.4, 17.6, 16.7, 17.1, 21.4, 20.1, 17.9, 16.8, 23.1. Test the hypothesis that the data came from some normal population at a significance level = 0.1.

=== Test questions for final assessment in the course ===

# Providing full example of two sequences (in case of computational overhead, you can approximate at the first decimal digit). Compute their:
## Covariance.
## Pearson’s correlation coefficient.
## Spearman’s Rank Correlation Coefficient.
## Kendall’s tau Correlation coefficient.
# What is an empirical distribution?
# Present, prove, and discuss the evaluation of the asymptotic confidence interval for the empirical distribution, detailing the role of the binomial.
# Prove, under the simplified hypotheses, the distribution free property of Dn.
# Write the Shannon Theorem and discuss its implications.
# Discuss how we could proceed to compute the confidence interval of the Kendall Tau correlation coefficient of the population.
# Suppose that you have the following datapoints: 0.4, 2, 0.6, 2.4, 2.2, 3.6, 3.8, 4; compute Dn to determine if they come from the uniform distribution [0,4].
# Prove that <math display="inline">\tilde{F}_n</math> is a consistent and unbiased estimator of F.

MSc: Advanced Statistics

2022-04-21T14:28:30Z

R.akhmetzyanov: /* Prerequisites */

= Advanced Statistics =

* '''Course name:''' Advanced Statistics
* '''Course number:''' DS-03

== Prerequisites ==

This course will benefit from good English language skills. Also it could be great if you acquire basic statistical skills in the perspective of [https://en.wikipedia.org/wiki/Empirical_research Empirical research]: statistical hypothesis testing, dependent and independent variables, distributions, etc..

* [https://eduwiki.innopolis.university/index.php/MSc:_Empirical_Methods CSE329 — Empirical Methods]

== Course Characteristics ==
=== Key concepts of the class ===

* Statistical inference
* Non parametric statistics
* Test of statistical hypotheses
* Simple linear regression and correlation analysis
* Meta-Analysis

=== What is the purpose of this course? ===

The main purpose of this course is to present the fundamentals of inferential statistics to the future software engineers and data scientists, on one side providing the scientific fundamentals of the disciplines, and on the other anchoring the theoretical concepts on practices coming from the world of software development and engineering. The course covers the statistical analysis of data with limited assumptions on the distribution, with reference to testing hypotheses, measuring correlations, building samples, and performing regressions.

=== Course objectives based on Bloom’s taxonomy ===

=== - What should a student remember at the end of the course? ===

By the end of the course, the students should be able to:

* Remember the fundamentals of inferential statistics
* Remember the specifics and purpose of different hypothesis tests
* Distinguish between parametric and non parametric tests

=== - What should a student be able to understand at the end of the course? ===

By the end of the course, the students should be able to understand:

* the basic concepts of inferential statistics
* the fundamental laws in statistics
* the concept of null and alternative hypotheses
* the hypotheses test procedure

=== - What should a student be able to apply at the end of the course? ===

By the end of the course, the students should be able to ...

* To understand the problems related to analyse statistically data not distributed normally
* To know the more recent computationally-intensive techniques that can help to describe samples and to infer properties of populations in absence of normality
* To identify situations when the data is on nominal scales so alternative techniques should be use, and act accordingly.
* To be able to run experiment to evaluate hypotheses for situation of scarce data, distributed non normally, on different kinds of scales.

=== Course evaluation (Standard) ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grade breakdown
!
!
!align="center"| '''Points'''
|-
| Weekly quizzes
|
|align="center"| 10
|-
| Midterm
|
|align="center"| 20
|-
| Final oral exam
|
|align="center"| 35
|-
| Final written exam
|
|align="center"| 30
|-
| Participation
|
|align="center"| 5
|}

=== Course evaluation (Project Based) ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grade breakdown
!
!
!align="center"| '''Points'''
|-
| Weekly quizzes
|
|align="center"| 15
|-
| Weekly Projects Review
|
|align="center"| 15
|-
| Mid of Semester Project Review
|
|align="center"| 20
|-
| Final Report
|
|align="center"| 30
|-
| Final Presentation with Q&A
|
|align="center"| 20
|}

=== Grades range ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course grading range
!
!
!align="center"| '''Range'''
|-
| A. Excellent
|
|align="center"| 95-100
|-
| B. Good
|
|align="center"| 75-94
|-
| C. Satisfactory
|
|align="center"| 55-74
|-
| D. Poor
|
|align="center"| 0-54
|}

=== Cooperation policy and quotations ===
We encourage vigorous discussion and cooperation in this class. You should feel free to discuss any aspects of the class with any classmates. However, we insist that any written material that is not specifically designated as a Team Deliverable be done by you alone. This includes answers to reading questions, individual reports associated with assignments, and labs. We also insist that if you include verbatim text from any source, you clearly indicate it using standard conventions of quotation or indentation and a note to indicate the source.

=== Resources and reference material ===

* Wasserman L. (2006) All of Nonparametric Statistics. Springer
* Randles, R.H. and Wolfe, D.A. (1991). Introduction to the Theory of Nonparametric Statistics. Melbourne: Robert Krieger. (Ch.1‐Ch.4)
* Hastie, T. Tibshirani, R. and Friedman, J. (2008) The Elements of Statistical Learning 2ed. Springer
* Hollander, M. and Wolfe, D.A. (1999). Nonparametric Statistical Methods, 2nd ed. New York: John Wiley.

== Course Sections ==

The main sections of the course and approximate hour distribution between them is as follows:

{| style="border-spacing: 2px; border: 1px solid darkgray;"
|+ Course Sections
!align="center"| '''Section'''
! '''Section Title'''
!align="center"| '''Teaching Hours'''
|-
|align="center"| 1
| Sampling Distributions Associated with the Normal Population
|align="center"| 15
|-
|align="center"| 2
| Test of Statistical Hypotheses
|align="center"| 30
|-
|align="center"| 3
| Simple Linear Regression and Correlation Analysis
|align="center"| 15
|}

=== Section 1 ===

==== Section title: ====

Sampling Distributions Associated with the Normal Population

=== Topics covered in this section: ===

* Introduction to the course, toward inference
* Student’s t-distribution
* Bernoulli and binomial distribution
* Chi-square distribution
* Snedecor’s F-distribution

=== What forms of evaluation were used to test students’ performance in this section? ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
!align="center"| '''Evaluation'''
!align="center"| '''Yes/No'''
|-
| Development of individual parts of software product code
|align="center"| 0
|-
| Homework and group projects
|align="center"| 0
|-
| Midterm evaluation
|align="center"| 1
|-
| Testing (written or computer based)
|align="center"| 1
|-
| Reports
|align="center"| 0
|-
| Essays
|align="center"| 0
|-
| Oral polls
|align="center"| 0
|-
| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Deduce the probability mass function <math display="inline">P(X \leq k</math> for a binomial distribution?
# Let X1,...,Xk be ''k'' iid random variables distributed with a <math display="inline">\chi^2</math> distribution with n1,...nk degrees of freedom respectively. 
What is the distribution of Y=X1+...+Xk? Define it precisely and prove the answer formally?
# List at least 3 random variables that “tend to follow” a t distribution?
# If X has Chi square function with the 5 degrees of freedom, then what is the probability that X is between 1.145 and 12.83?
# If X has a gamma distribution of (1,1), then what is the probability density function of the random variable 2X?

=== Typical questions for seminar classes (labs) within this section ===

# Define and provide examples of sample space, events and probability measure.
# Write the formula for the coefficients of the simple linear regression. Explain the mathematical procedure you do to derive them and derive them.
# Calculate the correlation between two functions and explain its meaning.
# Calculate the Pearson coefficient for the given functions.
# Deduce the MGF for normal distribution.
# State and prove the Bonferroni inequality.

=== Test questions for final assessment in this section ===

== Test of Statistical Hypotheses ==

=== Topics covered in this section: ===

* Z-test
* Student’s t-test
* Chi-square test
* Snedecor’s F-test

=== What forms of evaluation were used to test students’ performance in this section? ===

=== Typical questions for ongoing performance evaluation within this section ===

# Define the concept of power of a statistical test.
# Define the purpose of the F Test, its hypotheses, and its structure.
# Define the purpose of the t-Test, its hypotheses, and its structure.
# Define the purpose of the Chi square Test, its hypotheses, and its structure.
# Define the purpose of the Z Test, its hypotheses, and its structure.
# Provide concrete numeric examples with explanation on why the power of a test depends on:
## the size of the data sets.
## the magnitude of the effect.
## the level of statistical significance.
# Given a statistical test for which we have set a value <math display="inline">\alpha</math> we obtain a p:
## if we can reject H0 <math display="inline">(p < \alpha)</math>, what we typically say about H0 and H1.
## if we cannot reject H0 <math display="inline">(P \geq \alpha)</math>, what we can typically say about H0 and H1.
## when can we say that H0 holds?
## when can we say that H1 holds?

=== Typical questions for seminar classes (labs) within this section ===

# Provide a concrete example of a t test, detailing both H0 and H1.
# Present the structure of the F test for the analysis of the variance.
# Explain what are H0 and H1 in hypothesis testing.
# Explain the role of the Bonferroni inequality in hypothesis testing.

=== Test questions for final assessment in this section ===

== Simple Linear Regression and Correlation Analysis ==

==== Topics covered in this section: ====

* Kolmogorov-Smirnov test
* Size of samples, Kolmogorov-Smirnov, Fisher exact
* Logistic regression

=== What forms of evaluation were used to test students’ performance in this section? ===

{| style="border-spacing: 2px; border: 1px solid darkgray;"
!align="center"| '''Evaluation'''
!align="center"| '''Yes/No'''
|-
| Development of individual parts of software product code
|align="center"| 0
|-
| Homework and group projects
|align="center"| 0
|-
| Midterm evaluation
|align="center"| 0
|-
| Testing (written or computer based)
|align="center"| 1
|-
| Reports
|align="center"| 0
|-
| Essays
|align="center"| 0
|-
| Oral polls
|align="center"| 0
|-
| Discussions
|align="center"| 1
|}

=== Typical questions for ongoing performance evaluation within this section ===

# Let X1,X2, ...,X10 be a random sample from a distribution whose probability density function is <math display="inline">f(x) = (1 \quad if \;0 < x < 1</math>, otherwise 0). Based on the observed values 0.62, 0.36, 0.23, 0.76, 0.65, 0.09, 0.55, 0.26, 0.38, 0.24, test the hypothesis H0 : X UNIF(0, 1) against H1 : X UNIF(0, 1) at a significance level = 0.1.
# If X1,X2, ...,Xn is a random sample from a distribution with density function <math display="inline">f(x) = ((1-\theta)x^\theta \; if \; 0 < x < 1</math>, otherwise 0), what is the maximum likelihood estimator of <math display="inline">\theta</math>?
# Let X1,X2, ...,Xn be a random sample of size n from a distribution with a probability density function <math display="inline">f(x) = ((1-\theta)x^\theta \; if \; 0 < x < 1,</math> otherwise 0), where <math display="inline">0 < \theta</math> is a parameter. Using the maximum likelihood method find an estimator for the parameter <math display="inline">\theta</math>.
# Suppose you are told that the likelihood of <math display="inline">\theta</math> at <math display="inline">\theta=2</math> is given by 1/4. Is this the probability that <math display="inline">\theta=2</math>? Explain why or why not.

=== Typical questions for seminar classes (labs) within this section ===

# If X1,X2, ...,Xn is a random sample from a distribution with density function<math display="inline">f(x) = (\frac{1}{\theta} \;if \; 0 < x < 1,</math> otherwise 0), then what is the maximum likelihood estimator of <math display="inline">\theta</math>?
# Let X1,X2, ...,Xn be a random sample from a normal population with mean <math display="inline">\mu</math> and variance <math display="inline">\sigma^2</math>. What are the maximum likelihood estimators of <math display="inline">\mu</math> and <math display="inline">\sigma^2</math>?
# Suppose that you have the following data points: 0.36, 0.32, 0.10, 0.13, 0.45, 0.11, 0.12, 0.09; compute Dn to determine if they come from the uniform distribution [0,0.5].
# The data on the heights of 12 infants are given below: 18.2, 21.4, 22.6, 17.4, 17.6, 16.7, 17.1, 21.4, 20.1, 17.9, 16.8, 23.1. Test the hypothesis that the data came from some normal population at a significance level = 0.1.

=== Test questions for final assessment in the course ===

# Providing full example of two sequences (in case of computational overhead, you can approximate at the first decimal digit). Compute their:
## Covariance.
## Pearson’s correlation coefficient.
## Spearman’s Rank Correlation Coefficient.
## Kendall’s tau Correlation coefficient.
# What is an empirical distribution?
# Present, prove, and discuss the evaluation of the asymptotic confidence interval for the empirical distribution, detailing the role of the binomial.
# Prove, under the simplified hypotheses, the distribution free property of Dn.
# Write the Shannon Theorem and discuss its implications.
# Discuss how we could proceed to compute the confidence interval of the Kendall Tau correlation coefficient of the population.
# Suppose that you have the following datapoints: 0.4, 2, 0.6, 2.4, 2.2, 3.6, 3.8, 4; compute Dn to determine if they come from the uniform distribution [0,4].
# Prove that <math display="inline">\tilde{F}_n</math> is a consistent and unbiased estimator of F.

MSc: Large Systems

2022-04-21T14:11:47Z

R.akhmetzyanov: /* Prerequisites: */