IU - User contributions [en]

MSc: Advanced Statistics

2026-01-30T06:14:18Z

N.zlatanov: /* Course activities and grading breakdown */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are two requirements for passing this course:.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz/Assignment during each lecture (weekly evaluations) || 10
|-
| Labs classes (weekly evaluations) || 20
|-
| Midterm || 25
|-
| Final exam || 45
|}

=== Plagiarism Rules ===

* If a student submits a solution to a weekly assignment/quiz and/or lab that is identical to the one submitted from another student, then both students will obtain the maximum points for this task but with the negative sign.

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures and questions related to parts of the material that you find unclear.
* Submit solutions to the weekly quizzes.
* Submit the weekly lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly quizzes and weekly labs.

=== Final assessment ===

The final assessment is in a written form. You mast have at least 50% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in a written form.

MSc:SecurityOfSystemsAndNetworks

2025-09-01T09:10:51Z

N.zlatanov: /* Key concepts of the class */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 36
|-
| Project || 20
|-
| Weekly Quiz || 4
|-
| Exam || 40
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 50% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 50% point of a lab, must retake the lab, in which case the highest score will be 55% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 30%
|-
| 3 || Protocols || 30%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 24h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

MSc:SecurityOfSystemsAndNetworks

2025-09-01T09:10:29Z

N.zlatanov:

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 36
|-
| Project || 20
|-
| Weekly Quiz || 4
|-
| Exam || 40
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 50% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 50% point of a lab, must retake the lab, in which case the highest score will be 55% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 30%
|-
| 3 || Protocols || 30%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 24h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

MSc:SecurityOfSystemsAndNetworks

2025-09-01T09:10:16Z

N.zlatanov:

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 36
|-
| Project || 20
|-
| Weekly Quiz || 4
|-
| Exam || 40
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 50% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 50% point of a lab, must retake the lab, in which case the highest score will be 55% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 30%
|-
| 3 || Protocols || 30%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 24h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

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

# As above

MSc:SecurityOfSystemsAndNetworks

2025-09-01T09:10:04Z

N.zlatanov:

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 36
|-
| Project || 20
|-
| Weekly Quiz || 4
|-
| Exam || 40
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 50% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 50% point of a lab, must retake the lab, in which case the highest score will be 55% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 30%
|-
| 3 || Protocols || 30%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 24h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2025-09-01T09:09:48Z

N.zlatanov:

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 36
|-
| Project || 20
|-
| Weekly Quiz || 4
|-
| Exam || 40
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 50% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 50% point of a lab, must retake the lab, in which case the highest score will be 55% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 30%
|-
| 3 || Protocols || 30%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 24h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2025-09-01T09:08:55Z

N.zlatanov: /* Course Sections */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 36
|-
| Project || 20
|-
| Weekly Quiz || 4
|-
| Exam || 40
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 50% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 50% point of a lab, must retake the lab, in which case the highest score will be 55% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 30%
|-
| 3 || Protocols || 30%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 24h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2025-09-01T09:06:10Z

N.zlatanov: /* Lab policy: */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 36
|-
| Project || 20
|-
| Weekly Quiz || 4
|-
| Exam || 40
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 50% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 50% point of a lab, must retake the lab, in which case the highest score will be 55% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 24h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2024-08-26T07:07:14Z

N.zlatanov: /* To pass the course, you need: */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 36
|-
| Project || 20
|-
| Weekly Quiz || 4
|-
| Exam || 40
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 50% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 60% point of a lab, must retake the lab, in which case the highest score will be 65% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 24h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2024-08-26T03:33:33Z

N.zlatanov: /* Course evaluation */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 36
|-
| Project || 20
|-
| Weekly Quiz || 4
|-
| Exam || 40
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 60% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 60% point of a lab, must retake the lab, in which case the highest score will be 65% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 24h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2024-08-26T03:28:47Z

N.zlatanov: /* Course Sections */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 35
|-
| Project || 20
|-
| Weekly Quiz || 5
|-
| Exam || 40
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 60% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 60% point of a lab, must retake the lab, in which case the highest score will be 65% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 24h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2024-08-26T03:27:47Z

N.zlatanov: /* Course evaluation */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 35
|-
| Project || 20
|-
| Weekly Quiz || 5
|-
| Exam || 40
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 60% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 60% point of a lab, must retake the lab, in which case the highest score will be 65% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2024-08-26T03:26:28Z

N.zlatanov: /* Security of systems and networks */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 30
|-
| Project || 30
|-
| Weekly Quiz || 10
|-
| Exam || 30
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 60% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 60% point of a lab, must retake the lab, in which case the highest score will be 65% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc: Advanced Statistics

2024-01-22T09:12:43Z

N.zlatanov: /* Recommendations for students on how to succeed in the course */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are two requirements for passing this course:.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz/Assignment during each lecture (weekly evaluations) || 20
|-
| Labs classes (weekly evaluations) || 20
|-
| Midterm || 20
|-
| Final exam || 40
|}

=== Plagiarism Rules ===

* If a student submits a solution to a weekly assignment/quiz and/or lab that is identical to the one submitted from another student, then both students will obtain the maximum points for this task but with the negative sign.

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures and questions related to parts of the material that you find unclear.
* Submit solutions to the weekly quizzes.
* Submit the weekly lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly quizzes and weekly labs.

=== Final assessment ===

The final assessment is in a written form. You mast have at least 50% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in a written form.

MSc: Advanced Statistics

2024-01-22T09:12:15Z

N.zlatanov: /* Recommendations for students on how to succeed in the course */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are two requirements for passing this course:.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz/Assignment during each lecture (weekly evaluations) || 20
|-
| Labs classes (weekly evaluations) || 20
|-
| Midterm || 20
|-
| Final exam || 40
|}

=== Plagiarism Rules ===

* If a student submits a solution to a weekly assignment/quiz and/or lab that is identical to the one submitted from another student, then both students will obtain the maximum points for this task but with the negative sign.

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures and questions related to parts of the material that you find unclear.
* Submit solutions to the weekly quizzes.
* Submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly quizzes and weekly labs.

=== Final assessment ===

The final assessment is in a written form. You mast have at least 50% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in a written form.

MSc: Advanced Statistics

2024-01-22T09:11:59Z

N.zlatanov: /* Recommendations for students on how to succeed in the course */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are two requirements for passing this course:.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz/Assignment during each lecture (weekly evaluations) || 20
|-
| Labs classes (weekly evaluations) || 20
|-
| Midterm || 20
|-
| Final exam || 40
|}

=== Plagiarism Rules ===

* If a student submits a solution to a weekly assignment/quiz and/or lab that is identical to the one submitted from another student, then both students will obtain the maximum points for this task but with the negative sign.

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures and and questions related to parts of the material that you find unclear.
* Submit solutions to the weekly quizzes.
* Submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly quizzes and weekly labs.

=== Final assessment ===

The final assessment is in a written form. You mast have at least 50% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in a written form.

MSc: Advanced Statistics

2024-01-22T09:08:10Z

N.zlatanov: /* Plagiarism Rules */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are two requirements for passing this course:.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz/Assignment during each lecture (weekly evaluations) || 20
|-
| Labs classes (weekly evaluations) || 20
|-
| Midterm || 20
|-
| Final exam || 40
|}

=== Plagiarism Rules ===

* If a student submits a solution to a weekly assignment/quiz and/or lab that is identical to the one submitted from another student, then both students will obtain the maximum points for this task but with the negative sign.

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures
* Ask questions and provide answers to the questions during the onsite lectures.
* Attend all of the labs and submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly quizzes and weekly labs.

=== Final assessment ===

The final assessment is in a written form. You mast have at least 50% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in a written form.

MSc: Advanced Statistics

2024-01-22T09:07:56Z

N.zlatanov: /* Grading */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are two requirements for passing this course:.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz/Assignment during each lecture (weekly evaluations) || 20
|-
| Labs classes (weekly evaluations) || 20
|-
| Midterm || 20
|-
| Final exam || 40
|}

=== Plagiarism Rules ===

# If a student submits a solution to a weekly assignment/quiz and/or lab that is identical to the one submitted from another student, then both students will obtain the maximum points for this task but with the negative sign.

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures
* Ask questions and provide answers to the questions during the onsite lectures.
* Attend all of the labs and submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly quizzes and weekly labs.

=== Final assessment ===

The final assessment is in a written form. You mast have at least 50% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in a written form.

MSc: Advanced Statistics

2024-01-22T09:03:37Z

N.zlatanov: /* Ongoing performance assessment */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are two requirements for passing this course:.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz/Assignment during each lecture (weekly evaluations) || 20
|-
| Labs classes (weekly evaluations) || 20
|-
| Midterm || 20
|-
| Final exam || 40
|}

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures
* Ask questions and provide answers to the questions during the onsite lectures.
* Attend all of the labs and submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly quizzes and weekly labs.

=== Final assessment ===

The final assessment is in a written form. You mast have at least 50% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in a written form.

MSc: Advanced Statistics

2024-01-22T09:02:52Z

N.zlatanov: /* Course activities and grading breakdown */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are two requirements for passing this course:.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz/Assignment during each lecture (weekly evaluations) || 20
|-
| Labs classes (weekly evaluations) || 20
|-
| Midterm || 20
|-
| Final exam || 40
|}

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures
* Ask questions and provide answers to the questions during the onsite lectures.
* Attend all of the labs and submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly questions and weekly labs

=== Final assessment ===

The final assessment is in a written form. You mast have at least 50% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in a written form.

MSc: Advanced Statistics

2024-01-22T09:01:30Z

N.zlatanov: /* Final assessment */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are two requirements for passing this course:.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz during each lecture (weekly evaluations) || 15
|-
| Labs classes (weekly evaluations) || 15
|-
| Midterm || 20
|-
| Final exam || 50
|}

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures
* Ask questions and provide answers to the questions during the onsite lectures.
* Attend all of the labs and submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly questions and weekly labs

=== Final assessment ===

The final assessment is in a written form. You mast have at least 50% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in a written form.

MSc: Advanced Statistics

2024-01-22T09:01:12Z

N.zlatanov: /* The retake exam */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are two requirements for passing this course:.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz during each lecture (weekly evaluations) || 15
|-
| Labs classes (weekly evaluations) || 15
|-
| Midterm || 20
|-
| Final exam || 50
|}

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures
* Ask questions and provide answers to the questions during the onsite lectures.
* Attend all of the labs and submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly questions and weekly labs

=== Final assessment ===

The final assessment is in a written form. You mast have at least 51% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in a written form.

MSc: Advanced Statistics

2024-01-22T09:01:03Z

N.zlatanov: /* The retake exam */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are two requirements for passing this course:.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz during each lecture (weekly evaluations) || 15
|-
| Labs classes (weekly evaluations) || 15
|-
| Midterm || 20
|-
| Final exam || 50
|}

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures
* Ask questions and provide answers to the questions during the onsite lectures.
* Attend all of the labs and submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly questions and weekly labs

=== Final assessment ===

The final assessment is in a written form. You mast have at least 51% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in written form.

MSc: Advanced Statistics

2024-01-22T09:00:34Z

N.zlatanov: /* Minimum Requirements For Passing The Course */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are two requirements for passing this course:.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz during each lecture (weekly evaluations) || 15
|-
| Labs classes (weekly evaluations) || 15
|-
| Midterm || 20
|-
| Final exam || 50
|}

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures
* Ask questions and provide answers to the questions during the onsite lectures.
* Attend all of the labs and submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly questions and weekly labs

=== Final assessment ===

The final assessment is in a written form. You mast have at least 51% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in oral form.

MSc: Advanced Statistics

2024-01-22T08:59:24Z

N.zlatanov: /* Prerequisite subjects */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* Excellent knowledge of probability and statistics.

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are four requirements for passing this course:
# You must attend all labs.
# You must submit all lab reports.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz during each lecture (weekly evaluations) || 15
|-
| Labs classes (weekly evaluations) || 15
|-
| Midterm || 20
|-
| Final exam || 50
|}

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures
* Ask questions and provide answers to the questions during the onsite lectures.
* Attend all of the labs and submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly questions and weekly labs

=== Final assessment ===

The final assessment is in a written form. You mast have at least 51% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in oral form.

MSc:SecurityOfSystemsAndNetworks

2023-08-30T08:06:42Z

N.zlatanov: /* Grades range */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)
* '''Course number:''' SNE-08

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 30
|-
| Project || 30
|-
| Weekly Quiz || 10
|-
| Exam || 30
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

===To pass the course, you need:===

# To achieve more than 50% on the final exam.
# To achieve more than 60% on each individual lab

=== Lab policy: ===

# Only one extension of the deadline is allowed for the labs
# Students with less than 60% point of a lab, must retake the lab, in which case the highest score will be 65% of the overall score of the lab

=== Retake: ===

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2023-08-30T08:05:07Z

N.zlatanov: /* Grades range */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)
* '''Course number:''' SNE-08

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 30
|-
| Project || 30
|-
| Weekly Quiz || 10
|-
| Exam || 30
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

To pass the course, you need:

# To achieve more than 50% on the final exam.
# To achieve more than 60% on each individual lab

Lab policy:

# Only one extension of the deadline is allowed for the labs
# Students with less than 60% point of a lab, must retake the lab, in which case the highest score will be 65% of the overall score of the lab

Retake:

The retake of the exam will be oral, but only if the labs have been finished.

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2023-08-28T17:48:43Z

N.zlatanov: /* What forms of evaluation were used to test students’ performance in this section? */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)
* '''Course number:''' SNE-08

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 30
|-
| Project || 30
|-
| Weekly Quiz || 10
|-
| Exam || 30
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2023-08-28T17:48:29Z

N.zlatanov: /* What forms of evaluation were used to test students’ performance in this section? */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)
* '''Course number:''' SNE-08

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 30
|-
| Project || 30
|-
| Weekly Quiz || 10
|-
| Exam || 30
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

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

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2023-08-28T17:48:15Z

N.zlatanov: /* What forms of evaluation were used to test students’ performance in this section? */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)
* '''Course number:''' SNE-08

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 30
|-
| Project || 30
|-
| Weekly Quiz || 10
|-
| Exam || 30
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

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

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

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

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2023-08-28T17:47:55Z

N.zlatanov: /* What forms of evaluation were used to test students’ performance in this section? */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)
* '''Course number:''' SNE-08

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 30
|-
| Project || 30
|-
| Weekly Quiz || 10
|-
| Exam || 30
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

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

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

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

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

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

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2023-08-28T17:47:17Z

N.zlatanov: /* Course evaluation */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)
* '''Course number:''' SNE-08

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs || 30
|-
| Project || 30
|-
| Weekly Quiz || 10
|-
| Exam || 30
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

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

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

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

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

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

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

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

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

BSc: Introduction To Computer Vision

2023-08-28T06:29:20Z

N.zlatanov: /* Course activities and grading breakdown */

BSc: Introduction To Computer Vision

2023-08-28T06:28:23Z

N.zlatanov: /* Activities and Teaching Methods */

BSc: Introduction To Computer Vision

2023-08-28T06:27:56Z

N.zlatanov: /* Formative Assessment and Course Activities */

BSc: Introduction To Computer Vision

2023-08-28T06:27:14Z

N.zlatanov: /* Course grading range */

= Introduction to Computer Vision =
* '''Course name''': Introduction to Computer Vision
* '''Code discipline''': XXX
* '''Subject area''':

== Short Description ==
This course covers the following concepts: Computer vision using machine learning models.

== Prerequisites ==

=== Prerequisite subjects ===

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Representation of images and videos ||
# Computer representation
# Rescaling/manipulating images
|-
| Image Classification ||
# Loss Functions
# Backpropagation
# Neural Networks
# Training
|-
| Convolutional Neural Networks ||
# Training
# Architectures
|-
| Recurrent Neural Networks ||
# Training
# Architectures
|-
| Image Segmentation and object detection ||
# Techniques
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
This course provides an introductory but detailed treatment of computer vision techniques using machine learning, with an emphasis on implementing the computer vision algorithms from the scratch and using them to solve real-world problems. The course will begin with the image representation, but will quickly transition to computer vision techniques using machine learning, finishing with image segmentation and object detection and recognition. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.

=== 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 ...
* Significant exposure to real-world implementations
* To develop research interest in the theory and application of computer vision

==== 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 ...
* Suitability of different computer vision models in different scenarios
* Ability to choose the right model for the given task

==== 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 ...
* Hands on experience to implement different models to know inside behavior
* Sufficient exposure to train and deploy model for the given task
* Fine tune the deployed model in the real-world settings
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 91-100 || -
|-
| B. Good || 78-90 || -
|-
| C. Satisfactory || 60-77 || -
|-
| D. Poor || 0-59 || -
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Weekly Labs || 55
|-
| Weekly Quizzes || 10
|-
| Midterm Exam || 15
|-
| Final Exam || 20
|}

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

== Resources, literature and reference materials ==

=== Open access resources ===
* Handouts supplied by the instructor
* Materials from the internet and research papers shared by instructor

=== 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
|-
| Development of individual parts of software product code || 1 || 1 || 1 || 1
|-
| Homework and group projects || 1 || 1 || 1 || 1
|-
| Midterm evaluation || 1 || 1 || 1 || 1
|-
| Testing (written or computer based) || 1 || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the color spaces and where it’s used? || 1
|-
| Question || What are the primary and secondary colors? || 1
|-
| Question || How image is formed into computers? || 1
|-
| Question || How you will convert the RGB to grayscale images || 1
|-
| Question || Loading and plotting the images in python environment || 0
|-
| Question || Convertion of different color spaces || 0
|-
| Question || How you find the skin in the images based on the color space models || 0
|-
| Question || how to find red eye dot in face using color space models || 0
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the challenges to perform histogram task? || 1
|-
| Question || Apply convolutional filter to calculate the response. || 1
|-
| Question || What kind of parameters are required to apply different image filters? || 1
|-
| Question || How you will compute the gradients of the image and its benefits? || 1
|-
| Question || Implement Otsu Method || 0
|-
| Question || Implement Sobel, Preweitt filters || 0
|-
| Question || Implement Canny edge detector || 0
|-
| Question || Perform analysis over the different filtering on the given images || 0
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How feature extractor works over the given image? || 1
|-
| Question || What is the difference between the feature extraction and descriptors? || 1
|-
| Question || Explain the examples of descriptors and feature extractors. || 1
|-
| Question || Write down the pros and cons of SIFT, HOG and Harris. || 1
|-
| Question || Implement template matching algorithm || 0
|-
| Question || Implement histogram of gradient using CV2 library || 0
|-
| Question || Implement of SIFT for the given task || 0
|-
| Question || Implement Harris corner detection || 0
|-
| Question || Analysis of different extractors for the given task || 0
|}
==== Section 4 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How classification task is different from detection task? || 1
|-
| Question || Explain the transfer learning mechanism for object detection task. || 1
|-
| Question || How many types of model exist for object tracking in videos. || 1
|-
| Question || Write down the pros and cons of YOLO, FCN and MDNet. || 1
|-
| Question || Implement YOLO using transfer learning mechanism || 0
|-
| Question || Implement GAN for MNIST dataset || 0
|-
| Question || Implement FCN and GOTURN || 0
|-
| Question || Analysis of different models for the given task || 0
|}
=== Final assessment ===
'''Section 1'''
# How you can distinguish different color spaces?
# Explain and provide the reason for the blind spot creation in human eye.
# In what scenarios computer vision is better than human vision?
# Write down different robotic application areas where computer vision is applied successfully.
'''Section 2'''
# Calculate the kernels for the given images
# Explain the difference between different filters
# What is image noise and how it contributes to make the computer vision task difficult?
# Apply different combination of the filters to achieve the required output of the given image.
'''Section 3'''
# How you distinguish different feature extractors and descriptors?
# What are the possible methods to detect the corners?
# How corners are useful to help the robotic vision task?
# How you will patch the different images to construct the map of the location?
'''Section 4'''
# What are the loss functions used in YOLO?
# What are the learnable parameters of FCN for semantic segmentation?
# How semantic segmentation is different from instance segmentation?
# Write the application areas for object tracking in robotics.

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

'''Section 2'''

'''Section 3'''

'''Section 4'''

BSc: Introduction To Computer Vision

2023-08-28T06:26:29Z

N.zlatanov: /* Course activities and grading breakdown */

= Introduction to Computer Vision =
* '''Course name''': Introduction to Computer Vision
* '''Code discipline''': XXX
* '''Subject area''':

== Short Description ==
This course covers the following concepts: Computer vision using machine learning models.

== Prerequisites ==

=== Prerequisite subjects ===

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Representation of images and videos ||
# Computer representation
# Rescaling/manipulating images
|-
| Image Classification ||
# Loss Functions
# Backpropagation
# Neural Networks
# Training
|-
| Convolutional Neural Networks ||
# Training
# Architectures
|-
| Recurrent Neural Networks ||
# Training
# Architectures
|-
| Image Segmentation and object detection ||
# Techniques
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
This course provides an introductory but detailed treatment of computer vision techniques using machine learning, with an emphasis on implementing the computer vision algorithms from the scratch and using them to solve real-world problems. The course will begin with the image representation, but will quickly transition to computer vision techniques using machine learning, finishing with image segmentation and object detection and recognition. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.

=== 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 ...
* Significant exposure to real-world implementations
* To develop research interest in the theory and application of computer vision

==== 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 ...
* Suitability of different computer vision models in different scenarios
* Ability to choose the right model for the given task

==== 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 ...
* Hands on experience to implement different models to know inside behavior
* Sufficient exposure to train and deploy model for the given task
* Fine tune the deployed model in the real-world settings
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 91-100 || -
|-
| B. Good || 75-90 || -
|-
| C. Satisfactory || 60-74 || -
|-
| D. Poor || 0-59 || -
|}

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Weekly Labs || 55
|-
| Weekly Quizzes || 10
|-
| Midterm Exam || 15
|-
| Final Exam || 20
|}

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

== Resources, literature and reference materials ==

=== Open access resources ===
* Handouts supplied by the instructor
* Materials from the internet and research papers shared by instructor

=== 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
|-
| Development of individual parts of software product code || 1 || 1 || 1 || 1
|-
| Homework and group projects || 1 || 1 || 1 || 1
|-
| Midterm evaluation || 1 || 1 || 1 || 1
|-
| Testing (written or computer based) || 1 || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the color spaces and where it’s used? || 1
|-
| Question || What are the primary and secondary colors? || 1
|-
| Question || How image is formed into computers? || 1
|-
| Question || How you will convert the RGB to grayscale images || 1
|-
| Question || Loading and plotting the images in python environment || 0
|-
| Question || Convertion of different color spaces || 0
|-
| Question || How you find the skin in the images based on the color space models || 0
|-
| Question || how to find red eye dot in face using color space models || 0
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the challenges to perform histogram task? || 1
|-
| Question || Apply convolutional filter to calculate the response. || 1
|-
| Question || What kind of parameters are required to apply different image filters? || 1
|-
| Question || How you will compute the gradients of the image and its benefits? || 1
|-
| Question || Implement Otsu Method || 0
|-
| Question || Implement Sobel, Preweitt filters || 0
|-
| Question || Implement Canny edge detector || 0
|-
| Question || Perform analysis over the different filtering on the given images || 0
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How feature extractor works over the given image? || 1
|-
| Question || What is the difference between the feature extraction and descriptors? || 1
|-
| Question || Explain the examples of descriptors and feature extractors. || 1
|-
| Question || Write down the pros and cons of SIFT, HOG and Harris. || 1
|-
| Question || Implement template matching algorithm || 0
|-
| Question || Implement histogram of gradient using CV2 library || 0
|-
| Question || Implement of SIFT for the given task || 0
|-
| Question || Implement Harris corner detection || 0
|-
| Question || Analysis of different extractors for the given task || 0
|}
==== Section 4 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How classification task is different from detection task? || 1
|-
| Question || Explain the transfer learning mechanism for object detection task. || 1
|-
| Question || How many types of model exist for object tracking in videos. || 1
|-
| Question || Write down the pros and cons of YOLO, FCN and MDNet. || 1
|-
| Question || Implement YOLO using transfer learning mechanism || 0
|-
| Question || Implement GAN for MNIST dataset || 0
|-
| Question || Implement FCN and GOTURN || 0
|-
| Question || Analysis of different models for the given task || 0
|}
=== Final assessment ===
'''Section 1'''
# How you can distinguish different color spaces?
# Explain and provide the reason for the blind spot creation in human eye.
# In what scenarios computer vision is better than human vision?
# Write down different robotic application areas where computer vision is applied successfully.
'''Section 2'''
# Calculate the kernels for the given images
# Explain the difference between different filters
# What is image noise and how it contributes to make the computer vision task difficult?
# Apply different combination of the filters to achieve the required output of the given image.
'''Section 3'''
# How you distinguish different feature extractors and descriptors?
# What are the possible methods to detect the corners?
# How corners are useful to help the robotic vision task?
# How you will patch the different images to construct the map of the location?
'''Section 4'''
# What are the loss functions used in YOLO?
# What are the learnable parameters of FCN for semantic segmentation?
# How semantic segmentation is different from instance segmentation?
# Write the application areas for object tracking in robotics.

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

'''Section 2'''

'''Section 3'''

'''Section 4'''

BSc: Introduction To Computer Vision

2023-08-28T06:26:14Z

N.zlatanov: /* Course activities and grading breakdown */

= Introduction to Computer Vision =
* '''Course name''': Introduction to Computer Vision
* '''Code discipline''': XXX
* '''Subject area''':

== Short Description ==
This course covers the following concepts: Computer vision using machine learning models.

== Prerequisites ==

=== Prerequisite subjects ===

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Representation of images and videos ||
# Computer representation
# Rescaling/manipulating images
|-
| Image Classification ||
# Loss Functions
# Backpropagation
# Neural Networks
# Training
|-
| Convolutional Neural Networks ||
# Training
# Architectures
|-
| Recurrent Neural Networks ||
# Training
# Architectures
|-
| Image Segmentation and object detection ||
# Techniques
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
This course provides an introductory but detailed treatment of computer vision techniques using machine learning, with an emphasis on implementing the computer vision algorithms from the scratch and using them to solve real-world problems. The course will begin with the image representation, but will quickly transition to computer vision techniques using machine learning, finishing with image segmentation and object detection and recognition. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.

=== 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 ...
* Significant exposure to real-world implementations
* To develop research interest in the theory and application of computer vision

==== 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 ...
* Suitability of different computer vision models in different scenarios
* Ability to choose the right model for the given task

==== 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 ...
* Hands on experience to implement different models to know inside behavior
* Sufficient exposure to train and deploy model for the given task
* Fine tune the deployed model in the real-world settings
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 91-100 || -
|-
| B. Good || 75-90 || -
|-
| 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 || 55
|-
| Weekly Quizzes || 10
|-
| Midterm Exam || 15
|-
| Final Exam || 20
|}

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

== Resources, literature and reference materials ==

=== Open access resources ===
* Handouts supplied by the instructor
* Materials from the internet and research papers shared by instructor

=== 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
|-
| Development of individual parts of software product code || 1 || 1 || 1 || 1
|-
| Homework and group projects || 1 || 1 || 1 || 1
|-
| Midterm evaluation || 1 || 1 || 1 || 1
|-
| Testing (written or computer based) || 1 || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the color spaces and where it’s used? || 1
|-
| Question || What are the primary and secondary colors? || 1
|-
| Question || How image is formed into computers? || 1
|-
| Question || How you will convert the RGB to grayscale images || 1
|-
| Question || Loading and plotting the images in python environment || 0
|-
| Question || Convertion of different color spaces || 0
|-
| Question || How you find the skin in the images based on the color space models || 0
|-
| Question || how to find red eye dot in face using color space models || 0
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the challenges to perform histogram task? || 1
|-
| Question || Apply convolutional filter to calculate the response. || 1
|-
| Question || What kind of parameters are required to apply different image filters? || 1
|-
| Question || How you will compute the gradients of the image and its benefits? || 1
|-
| Question || Implement Otsu Method || 0
|-
| Question || Implement Sobel, Preweitt filters || 0
|-
| Question || Implement Canny edge detector || 0
|-
| Question || Perform analysis over the different filtering on the given images || 0
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How feature extractor works over the given image? || 1
|-
| Question || What is the difference between the feature extraction and descriptors? || 1
|-
| Question || Explain the examples of descriptors and feature extractors. || 1
|-
| Question || Write down the pros and cons of SIFT, HOG and Harris. || 1
|-
| Question || Implement template matching algorithm || 0
|-
| Question || Implement histogram of gradient using CV2 library || 0
|-
| Question || Implement of SIFT for the given task || 0
|-
| Question || Implement Harris corner detection || 0
|-
| Question || Analysis of different extractors for the given task || 0
|}
==== Section 4 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How classification task is different from detection task? || 1
|-
| Question || Explain the transfer learning mechanism for object detection task. || 1
|-
| Question || How many types of model exist for object tracking in videos. || 1
|-
| Question || Write down the pros and cons of YOLO, FCN and MDNet. || 1
|-
| Question || Implement YOLO using transfer learning mechanism || 0
|-
| Question || Implement GAN for MNIST dataset || 0
|-
| Question || Implement FCN and GOTURN || 0
|-
| Question || Analysis of different models for the given task || 0
|}
=== Final assessment ===
'''Section 1'''
# How you can distinguish different color spaces?
# Explain and provide the reason for the blind spot creation in human eye.
# In what scenarios computer vision is better than human vision?
# Write down different robotic application areas where computer vision is applied successfully.
'''Section 2'''
# Calculate the kernels for the given images
# Explain the difference between different filters
# What is image noise and how it contributes to make the computer vision task difficult?
# Apply different combination of the filters to achieve the required output of the given image.
'''Section 3'''
# How you distinguish different feature extractors and descriptors?
# What are the possible methods to detect the corners?
# How corners are useful to help the robotic vision task?
# How you will patch the different images to construct the map of the location?
'''Section 4'''
# What are the loss functions used in YOLO?
# What are the learnable parameters of FCN for semantic segmentation?
# How semantic segmentation is different from instance segmentation?
# Write the application areas for object tracking in robotics.

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

'''Section 2'''

'''Section 3'''

'''Section 4'''

BSc: Introduction To Computer Vision

2023-08-28T06:24:56Z

N.zlatanov: /* Course grading range */

= Introduction to Computer Vision =
* '''Course name''': Introduction to Computer Vision
* '''Code discipline''': XXX
* '''Subject area''':

== Short Description ==
This course covers the following concepts: Computer vision using machine learning models.

== Prerequisites ==

=== Prerequisite subjects ===

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Representation of images and videos ||
# Computer representation
# Rescaling/manipulating images
|-
| Image Classification ||
# Loss Functions
# Backpropagation
# Neural Networks
# Training
|-
| Convolutional Neural Networks ||
# Training
# Architectures
|-
| Recurrent Neural Networks ||
# Training
# Architectures
|-
| Image Segmentation and object detection ||
# Techniques
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
This course provides an introductory but detailed treatment of computer vision techniques using machine learning, with an emphasis on implementing the computer vision algorithms from the scratch and using them to solve real-world problems. The course will begin with the image representation, but will quickly transition to computer vision techniques using machine learning, finishing with image segmentation and object detection and recognition. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.

=== 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 ...
* Significant exposure to real-world implementations
* To develop research interest in the theory and application of computer vision

==== 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 ...
* Suitability of different computer vision models in different scenarios
* Ability to choose the right model for the given task

==== 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 ...
* Hands on experience to implement different models to know inside behavior
* Sufficient exposure to train and deploy model for the given task
* Fine tune the deployed model in the real-world settings
== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 91-100 || -
|-
| B. Good || 75-90 || -
|-
| 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
|-
| Interim performance assessment || 50
|-
| Exams || 30
|}

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

== Resources, literature and reference materials ==

=== Open access resources ===
* Handouts supplied by the instructor
* Materials from the internet and research papers shared by instructor

=== 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
|-
| Development of individual parts of software product code || 1 || 1 || 1 || 1
|-
| Homework and group projects || 1 || 1 || 1 || 1
|-
| Midterm evaluation || 1 || 1 || 1 || 1
|-
| Testing (written or computer based) || 1 || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the color spaces and where it’s used? || 1
|-
| Question || What are the primary and secondary colors? || 1
|-
| Question || How image is formed into computers? || 1
|-
| Question || How you will convert the RGB to grayscale images || 1
|-
| Question || Loading and plotting the images in python environment || 0
|-
| Question || Convertion of different color spaces || 0
|-
| Question || How you find the skin in the images based on the color space models || 0
|-
| Question || how to find red eye dot in face using color space models || 0
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the challenges to perform histogram task? || 1
|-
| Question || Apply convolutional filter to calculate the response. || 1
|-
| Question || What kind of parameters are required to apply different image filters? || 1
|-
| Question || How you will compute the gradients of the image and its benefits? || 1
|-
| Question || Implement Otsu Method || 0
|-
| Question || Implement Sobel, Preweitt filters || 0
|-
| Question || Implement Canny edge detector || 0
|-
| Question || Perform analysis over the different filtering on the given images || 0
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How feature extractor works over the given image? || 1
|-
| Question || What is the difference between the feature extraction and descriptors? || 1
|-
| Question || Explain the examples of descriptors and feature extractors. || 1
|-
| Question || Write down the pros and cons of SIFT, HOG and Harris. || 1
|-
| Question || Implement template matching algorithm || 0
|-
| Question || Implement histogram of gradient using CV2 library || 0
|-
| Question || Implement of SIFT for the given task || 0
|-
| Question || Implement Harris corner detection || 0
|-
| Question || Analysis of different extractors for the given task || 0
|}
==== Section 4 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How classification task is different from detection task? || 1
|-
| Question || Explain the transfer learning mechanism for object detection task. || 1
|-
| Question || How many types of model exist for object tracking in videos. || 1
|-
| Question || Write down the pros and cons of YOLO, FCN and MDNet. || 1
|-
| Question || Implement YOLO using transfer learning mechanism || 0
|-
| Question || Implement GAN for MNIST dataset || 0
|-
| Question || Implement FCN and GOTURN || 0
|-
| Question || Analysis of different models for the given task || 0
|}
=== Final assessment ===
'''Section 1'''
# How you can distinguish different color spaces?
# Explain and provide the reason for the blind spot creation in human eye.
# In what scenarios computer vision is better than human vision?
# Write down different robotic application areas where computer vision is applied successfully.
'''Section 2'''
# Calculate the kernels for the given images
# Explain the difference between different filters
# What is image noise and how it contributes to make the computer vision task difficult?
# Apply different combination of the filters to achieve the required output of the given image.
'''Section 3'''
# How you distinguish different feature extractors and descriptors?
# What are the possible methods to detect the corners?
# How corners are useful to help the robotic vision task?
# How you will patch the different images to construct the map of the location?
'''Section 4'''
# What are the loss functions used in YOLO?
# What are the learnable parameters of FCN for semantic segmentation?
# How semantic segmentation is different from instance segmentation?
# Write the application areas for object tracking in robotics.

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

'''Section 2'''

'''Section 3'''

'''Section 4'''

BSc: Introduction To Computer Vision

2023-08-28T06:24:22Z

N.zlatanov: /* What is the main purpose of this course? */

= Introduction to Computer Vision =
* '''Course name''': Introduction to Computer Vision
* '''Code discipline''': XXX
* '''Subject area''':

== Short Description ==
This course covers the following concepts: Computer vision using machine learning models.

== Prerequisites ==

=== Prerequisite subjects ===

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Representation of images and videos ||
# Computer representation
# Rescaling/manipulating images
|-
| Image Classification ||
# Loss Functions
# Backpropagation
# Neural Networks
# Training
|-
| Convolutional Neural Networks ||
# Training
# Architectures
|-
| Recurrent Neural Networks ||
# Training
# Architectures
|-
| Image Segmentation and object detection ||
# Techniques
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
This course provides an introductory but detailed treatment of computer vision techniques using machine learning, with an emphasis on implementing the computer vision algorithms from the scratch and using them to solve real-world problems. The course will begin with the image representation, but will quickly transition to computer vision techniques using machine learning, finishing with image segmentation and object detection and recognition. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.

=== 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 ...
* Significant exposure to real-world implementations
* To develop research interest in the theory and application of computer vision

==== 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 ...
* Suitability of different computer vision models in different scenarios
* Ability to choose the right model for the given task

==== 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 ...
* Hands on experience to implement different models to know inside behavior
* Sufficient exposure to train and deploy model for the given task
* Fine tune the deployed model in the real-world settings
== 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
|-
| Interim performance assessment || 50
|-
| Exams || 30
|}

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

== Resources, literature and reference materials ==

=== Open access resources ===
* Handouts supplied by the instructor
* Materials from the internet and research papers shared by instructor

=== 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
|-
| Development of individual parts of software product code || 1 || 1 || 1 || 1
|-
| Homework and group projects || 1 || 1 || 1 || 1
|-
| Midterm evaluation || 1 || 1 || 1 || 1
|-
| Testing (written or computer based) || 1 || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the color spaces and where it’s used? || 1
|-
| Question || What are the primary and secondary colors? || 1
|-
| Question || How image is formed into computers? || 1
|-
| Question || How you will convert the RGB to grayscale images || 1
|-
| Question || Loading and plotting the images in python environment || 0
|-
| Question || Convertion of different color spaces || 0
|-
| Question || How you find the skin in the images based on the color space models || 0
|-
| Question || how to find red eye dot in face using color space models || 0
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the challenges to perform histogram task? || 1
|-
| Question || Apply convolutional filter to calculate the response. || 1
|-
| Question || What kind of parameters are required to apply different image filters? || 1
|-
| Question || How you will compute the gradients of the image and its benefits? || 1
|-
| Question || Implement Otsu Method || 0
|-
| Question || Implement Sobel, Preweitt filters || 0
|-
| Question || Implement Canny edge detector || 0
|-
| Question || Perform analysis over the different filtering on the given images || 0
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How feature extractor works over the given image? || 1
|-
| Question || What is the difference between the feature extraction and descriptors? || 1
|-
| Question || Explain the examples of descriptors and feature extractors. || 1
|-
| Question || Write down the pros and cons of SIFT, HOG and Harris. || 1
|-
| Question || Implement template matching algorithm || 0
|-
| Question || Implement histogram of gradient using CV2 library || 0
|-
| Question || Implement of SIFT for the given task || 0
|-
| Question || Implement Harris corner detection || 0
|-
| Question || Analysis of different extractors for the given task || 0
|}
==== Section 4 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How classification task is different from detection task? || 1
|-
| Question || Explain the transfer learning mechanism for object detection task. || 1
|-
| Question || How many types of model exist for object tracking in videos. || 1
|-
| Question || Write down the pros and cons of YOLO, FCN and MDNet. || 1
|-
| Question || Implement YOLO using transfer learning mechanism || 0
|-
| Question || Implement GAN for MNIST dataset || 0
|-
| Question || Implement FCN and GOTURN || 0
|-
| Question || Analysis of different models for the given task || 0
|}
=== Final assessment ===
'''Section 1'''
# How you can distinguish different color spaces?
# Explain and provide the reason for the blind spot creation in human eye.
# In what scenarios computer vision is better than human vision?
# Write down different robotic application areas where computer vision is applied successfully.
'''Section 2'''
# Calculate the kernels for the given images
# Explain the difference between different filters
# What is image noise and how it contributes to make the computer vision task difficult?
# Apply different combination of the filters to achieve the required output of the given image.
'''Section 3'''
# How you distinguish different feature extractors and descriptors?
# What are the possible methods to detect the corners?
# How corners are useful to help the robotic vision task?
# How you will patch the different images to construct the map of the location?
'''Section 4'''
# What are the loss functions used in YOLO?
# What are the learnable parameters of FCN for semantic segmentation?
# How semantic segmentation is different from instance segmentation?
# Write the application areas for object tracking in robotics.

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

'''Section 2'''

'''Section 3'''

'''Section 4'''

BSc: Introduction To Computer Vision

2023-08-28T06:21:09Z

N.zlatanov: /* Course Topics */

= Introduction to Computer Vision =
* '''Course name''': Introduction to Computer Vision
* '''Code discipline''': XXX
* '''Subject area''':

== Short Description ==
This course covers the following concepts: Computer vision using machine learning models.

== Prerequisites ==

=== Prerequisite subjects ===

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Representation of images and videos ||
# Computer representation
# Rescaling/manipulating images
|-
| Image Classification ||
# Loss Functions
# Backpropagation
# Neural Networks
# Training
|-
| Convolutional Neural Networks ||
# Training
# Architectures
|-
| Recurrent Neural Networks ||
# Training
# Architectures
|-
| Image Segmentation and object detection ||
# Techniques
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
This course provides an intensive treatment of a cross-section of the key elements of computer vision, with an emphasis on implementing them in modern programming environments, and using them to solve real-world problems. The course will begin with the fundamentals of image processing and image filtering, but will quickly build to cover more advanced topics, including image segmentation, object detection and recognition, face detection, content-based image retrieval, artificial neural networks, convolutional neural networks, generative adversarial networks and much more. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.

=== 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 ...
* Significant exposure to real-world implementations
* To develop research interest in the theory and application of computer vision

==== 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 ...
* Suitability of different computer vision models in different scenarios
* Ability to choose the right model for the given task

==== 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 ...
* Hands on experience to implement different models to know inside behavior
* Sufficient exposure to train and deploy model for the given task
* Fine tune the deployed model in the real-world settings
== 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
|-
| Interim performance assessment || 50
|-
| Exams || 30
|}

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

== Resources, literature and reference materials ==

=== Open access resources ===
* Handouts supplied by the instructor
* Materials from the internet and research papers shared by instructor

=== 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
|-
| Development of individual parts of software product code || 1 || 1 || 1 || 1
|-
| Homework and group projects || 1 || 1 || 1 || 1
|-
| Midterm evaluation || 1 || 1 || 1 || 1
|-
| Testing (written or computer based) || 1 || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the color spaces and where it’s used? || 1
|-
| Question || What are the primary and secondary colors? || 1
|-
| Question || How image is formed into computers? || 1
|-
| Question || How you will convert the RGB to grayscale images || 1
|-
| Question || Loading and plotting the images in python environment || 0
|-
| Question || Convertion of different color spaces || 0
|-
| Question || How you find the skin in the images based on the color space models || 0
|-
| Question || how to find red eye dot in face using color space models || 0
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the challenges to perform histogram task? || 1
|-
| Question || Apply convolutional filter to calculate the response. || 1
|-
| Question || What kind of parameters are required to apply different image filters? || 1
|-
| Question || How you will compute the gradients of the image and its benefits? || 1
|-
| Question || Implement Otsu Method || 0
|-
| Question || Implement Sobel, Preweitt filters || 0
|-
| Question || Implement Canny edge detector || 0
|-
| Question || Perform analysis over the different filtering on the given images || 0
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How feature extractor works over the given image? || 1
|-
| Question || What is the difference between the feature extraction and descriptors? || 1
|-
| Question || Explain the examples of descriptors and feature extractors. || 1
|-
| Question || Write down the pros and cons of SIFT, HOG and Harris. || 1
|-
| Question || Implement template matching algorithm || 0
|-
| Question || Implement histogram of gradient using CV2 library || 0
|-
| Question || Implement of SIFT for the given task || 0
|-
| Question || Implement Harris corner detection || 0
|-
| Question || Analysis of different extractors for the given task || 0
|}
==== Section 4 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How classification task is different from detection task? || 1
|-
| Question || Explain the transfer learning mechanism for object detection task. || 1
|-
| Question || How many types of model exist for object tracking in videos. || 1
|-
| Question || Write down the pros and cons of YOLO, FCN and MDNet. || 1
|-
| Question || Implement YOLO using transfer learning mechanism || 0
|-
| Question || Implement GAN for MNIST dataset || 0
|-
| Question || Implement FCN and GOTURN || 0
|-
| Question || Analysis of different models for the given task || 0
|}
=== Final assessment ===
'''Section 1'''
# How you can distinguish different color spaces?
# Explain and provide the reason for the blind spot creation in human eye.
# In what scenarios computer vision is better than human vision?
# Write down different robotic application areas where computer vision is applied successfully.
'''Section 2'''
# Calculate the kernels for the given images
# Explain the difference between different filters
# What is image noise and how it contributes to make the computer vision task difficult?
# Apply different combination of the filters to achieve the required output of the given image.
'''Section 3'''
# How you distinguish different feature extractors and descriptors?
# What are the possible methods to detect the corners?
# How corners are useful to help the robotic vision task?
# How you will patch the different images to construct the map of the location?
'''Section 4'''
# What are the loss functions used in YOLO?
# What are the learnable parameters of FCN for semantic segmentation?
# How semantic segmentation is different from instance segmentation?
# Write the application areas for object tracking in robotics.

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

'''Section 2'''

'''Section 3'''

'''Section 4'''

BSc: Introduction To Computer Vision

2023-08-28T06:15:55Z

N.zlatanov: /* Short Description */

= Introduction to Computer Vision =
* '''Course name''': Introduction to Computer Vision
* '''Code discipline''': XXX
* '''Subject area''':

== Short Description ==
This course covers the following concepts: Computer vision using machine learning models.

== Prerequisites ==

=== Prerequisite subjects ===

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Image Acquisition and Basic Image Processing ||
# Computer vision in action
# The Human Vision System
# Optical Illusions
# Sampling and Quantization
# Image Representation
# Colour Spaces
|-
| Image Filtering and Binary Vision ||
# Image noise
# Convolutions and kernels
# Smoothing and blurring
# Thresholding and histograms
# Morphological operations
# Gradients and Edge detection
|-
| Feature Extractors and Descriptors ||
# Histogram of Gradients (HoG)
# Scale-invariant feature transform (SIFT)
# Harris corner detector
# Template matching
# Bag of visual words
# Face Detection and Recognition (Viola Johns)
|-
| Deep learning models for computer vision ||
# You Only Look Once: Unified, Real-Time Object Detection (YOLO)
# Generative Adversarial Networks (GAN)
# Fully Convolutional Networks (FCN) for semantic segmentation
# Multi Domain Network (MDNet) for object tracking
# Generic Object Tracking Using Regression Networks (GOTURN) for object tracking
|}
== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
This course provides an intensive treatment of a cross-section of the key elements of computer vision, with an emphasis on implementing them in modern programming environments, and using them to solve real-world problems. The course will begin with the fundamentals of image processing and image filtering, but will quickly build to cover more advanced topics, including image segmentation, object detection and recognition, face detection, content-based image retrieval, artificial neural networks, convolutional neural networks, generative adversarial networks and much more. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.

=== 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 ...
* Significant exposure to real-world implementations
* To develop research interest in the theory and application of computer vision

==== 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 ...
* Suitability of different computer vision models in different scenarios
* Ability to choose the right model for the given task

==== 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 ...
* Hands on experience to implement different models to know inside behavior
* Sufficient exposure to train and deploy model for the given task
* Fine tune the deployed model in the real-world settings
== 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
|-
| Interim performance assessment || 50
|-
| Exams || 30
|}

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

== Resources, literature and reference materials ==

=== Open access resources ===
* Handouts supplied by the instructor
* Materials from the internet and research papers shared by instructor

=== 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
|-
| Development of individual parts of software product code || 1 || 1 || 1 || 1
|-
| Homework and group projects || 1 || 1 || 1 || 1
|-
| Midterm evaluation || 1 || 1 || 1 || 1
|-
| Testing (written or computer based) || 1 || 1 || 1 || 1
|-
| Discussions || 1 || 1 || 1 || 1
|}
== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===

==== Section 1 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the color spaces and where it’s used? || 1
|-
| Question || What are the primary and secondary colors? || 1
|-
| Question || How image is formed into computers? || 1
|-
| Question || How you will convert the RGB to grayscale images || 1
|-
| Question || Loading and plotting the images in python environment || 0
|-
| Question || Convertion of different color spaces || 0
|-
| Question || How you find the skin in the images based on the color space models || 0
|-
| Question || how to find red eye dot in face using color space models || 0
|}
==== Section 2 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || What are the challenges to perform histogram task? || 1
|-
| Question || Apply convolutional filter to calculate the response. || 1
|-
| Question || What kind of parameters are required to apply different image filters? || 1
|-
| Question || How you will compute the gradients of the image and its benefits? || 1
|-
| Question || Implement Otsu Method || 0
|-
| Question || Implement Sobel, Preweitt filters || 0
|-
| Question || Implement Canny edge detector || 0
|-
| Question || Perform analysis over the different filtering on the given images || 0
|}
==== Section 3 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How feature extractor works over the given image? || 1
|-
| Question || What is the difference between the feature extraction and descriptors? || 1
|-
| Question || Explain the examples of descriptors and feature extractors. || 1
|-
| Question || Write down the pros and cons of SIFT, HOG and Harris. || 1
|-
| Question || Implement template matching algorithm || 0
|-
| Question || Implement histogram of gradient using CV2 library || 0
|-
| Question || Implement of SIFT for the given task || 0
|-
| Question || Implement Harris corner detection || 0
|-
| Question || Analysis of different extractors for the given task || 0
|}
==== Section 4 ====
{| class="wikitable"
|+
|-
! Activity Type !! Content !! Is Graded?
|-
| Question || How classification task is different from detection task? || 1
|-
| Question || Explain the transfer learning mechanism for object detection task. || 1
|-
| Question || How many types of model exist for object tracking in videos. || 1
|-
| Question || Write down the pros and cons of YOLO, FCN and MDNet. || 1
|-
| Question || Implement YOLO using transfer learning mechanism || 0
|-
| Question || Implement GAN for MNIST dataset || 0
|-
| Question || Implement FCN and GOTURN || 0
|-
| Question || Analysis of different models for the given task || 0
|}
=== Final assessment ===
'''Section 1'''
# How you can distinguish different color spaces?
# Explain and provide the reason for the blind spot creation in human eye.
# In what scenarios computer vision is better than human vision?
# Write down different robotic application areas where computer vision is applied successfully.
'''Section 2'''
# Calculate the kernels for the given images
# Explain the difference between different filters
# What is image noise and how it contributes to make the computer vision task difficult?
# Apply different combination of the filters to achieve the required output of the given image.
'''Section 3'''
# How you distinguish different feature extractors and descriptors?
# What are the possible methods to detect the corners?
# How corners are useful to help the robotic vision task?
# How you will patch the different images to construct the map of the location?
'''Section 4'''
# What are the loss functions used in YOLO?
# What are the learnable parameters of FCN for semantic segmentation?
# How semantic segmentation is different from instance segmentation?
# Write the application areas for object tracking in robotics.

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

'''Section 2'''

'''Section 3'''

'''Section 4'''

MSc:SecurityOfSystemsAndNetworks

2023-08-28T06:09:49Z

N.zlatanov: /* Typical questions for seminar classes (labs) within this section */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)
* '''Course number:''' SNE-08

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs/seminar classes || 30
|-
| Project || 35
|-
| Exam || 35
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

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

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

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

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

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

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

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

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2023-08-28T06:09:14Z

N.zlatanov: /* Typical questions for ongoing performance evaluation within this section */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)
* '''Course number:''' SNE-08

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs/seminar classes || 30
|-
| Project || 35
|-
| Exam || 35
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

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

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

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

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

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

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?

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

# Implement IPSec
# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

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

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2023-08-28T06:08:21Z

N.zlatanov: /* Grades range */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)
* '''Course number:''' SNE-08

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs/seminar classes || 30
|-
| Project || 35
|-
| Exam || 35
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 91-100
|-
| B. Good || 81-90
|-
| C. Satisfactory || 51-80
|-
| D. Poor || 0-50
|}

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

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

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

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

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

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

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?
# Why IPSec is so over-engineered? and what are the security flaws?
# What are different components of IPSec

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

# Implement IPSec
# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

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

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2023-08-28T06:07:07Z

N.zlatanov: /* What is the purpose of this course? */

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)
* '''Course number:''' SNE-08

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, crypto. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs/seminar classes || 30
|-
| Project || 35
|-
| Exam || 35
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 88-100
|-
| B. Good || 77-87
|-
| C. Satisfactory || 51-76
|-
| D. Poor || 0-50
|}

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

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

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

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

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

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

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?
# Why IPSec is so over-engineered? and what are the security flaws?
# What are different components of IPSec

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

# Implement IPSec
# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

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

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc:SecurityOfSystemsAndNetworks

2023-08-28T06:06:01Z

N.zlatanov: Created page with "= Security of systems and networks = * '''Course name:''' Security of systems and networks (SSN) * '''Course number:''' SNE-08 == Course character..."

= Security of systems and networks =

* '''Course name:''' Security of systems and networks (SSN)
* '''Course number:''' SNE-08

== Course characteristics ==

=== Key concepts of the class ===

* Applied cryptography
* Security protocols
* Network and Internet security
* Authentication and Authorization
* Software Security

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

This course will cover the fundamentals of security, security protocols, and their
applications in real-world. The topics covered in this course include applied
cryptography, authentication, passwords, practical security, social aspects of
security, SSL/TLS, email security, PKI, and IPSec. Furthermore, this course
will strengthen the security knowledge of the students and guide them in the
right direction for their upcoming research projects and advanced courses. The
course is divided into two parts. The first part will cover the theory and hands-on practice of the concepts taught at class. And the second part of the course
will focus on the course projects. The student will work on a security project
by using the concepts taught in the class.

== Prerequisites ==
* No specific prerequisites are mandated.

== Recommendations for students on how to succeed in the course ==
References:
* Attend the lectures
* Read the lecture notes
* Read the related chapters in the book

* Attend and finish the labs

* Finish your assigned project successfully

== 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:

* Demonstrate the acquired knowledge and skills in applied cryptography (symmetric and asymmetric cryptography),
* Operate classical enigma machine, encode and decode messages with it
* Demonstrate the working knowledge of famous cryptographic algorithms and discuss their shortcomings
* Demonstrate and operate the already implemented security protocols over internet,
* Reason about the problems in the security of networked systems and current internet and their existing solutions,
* Solve mathematical problems (especially in number theory),

=== 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:

* Crypt-analyze ciphertext and decrypt through frequency analysis and other important techniques
* Design security protocols
* Find security flaws in security protocols
* Get hands-on experience of the existing enterprise cryptographic algorithms and use them in projects,
* Demonstrate the skill of finding out security issues in networked systems and internet technologies,

=== Course evaluation ===

The acquired knowledge will be evaluated via labs, a project, and the exam, with points as in the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Type of Evaluation !! Points
|-
| Labs/seminar classes || 30
|-
| Project || 35
|-
| Exam || 35
|}

=== Grades range ===

The grades will be given according to the following table:

{| class="wikitable" style="margin:auto"
|+
|-
! Grade !! Range of points
|-
| A. Excellent || 88-100
|-
| B. Good || 77-87
|-
| C. Satisfactory || 51-76
|-
| D. Poor || 0-50
|}

=== Resources and reference material ===

* Lecture slides
* Book
* Links 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:

{| class="wikitable" style="margin:auto"
|+
|-
! Section !! Section Title !! Teaching Hours
|-
| 1 || Cryptography || 40%
|-
| 2 || Access Control || 25%
|-
| 3 || Protocols || 25%
|-
| 4 || Software || 10%
|-
| 5 || Labs|| 24h
|-
| 6 || Project|| 32h
|}

=== Section 1 title: ===

Cryptography

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

* Basics of Crypto
* Symmetric Key Crypto
* Public Key Crypto
* Hash Functions

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

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

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

# What are typical classic crypto methods?
# What are the differences in stream and block ciphers from performance standpoint?
# How to measure the security of cryptographic algorithms?
# How to encrypt and decrypt with different asymmetric crypto algorithms?
# How to embedd backdoors in crypto algorithms?
# How to realize key exchange through Hiffie-Hellman using traditional techniques and elliptic curve techniques?
# How to make security algorithms efficient?

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

# Make enigma machine with pringle box
# Assess the security of different setups of RSA
# Implement man in the middle attack
# Implement addition over elliptic curves
# Solve crypto math problems

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

# Same as above

=== Section 2 title: ===

Access Control

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

* Authentication
* Authorization

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

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

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

# What are pros and cons of using symmetric and asymmetric cryptographic mechanisms for authentication?
# What is man in the middle attack?
# Develop home-grown authentication mechanisms?
# How Kerberos reduces the communication overhead?
# Where is shibboleth used?

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

# Implement different variants of authentication protocols
# Find out security flaws in authentication protocols
# Identify shortcomings of different protocols

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

# Same as above

=== Section 3 title: ===

Protocols

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

* Simple Authentication Protocols
* Real-World Security Protocols

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

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

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

# How does SSL and TLS work?
# HOw does SSL and TLS combine symmetric and asymmetric cryptography?
# Why IPSec is so over-engineered? and what are the security flaws?
# What are different components of IPSec

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

# Implement IPSec
# Assess the security of SSL and TLS handshakes

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

# Same as above

=== Section 4 title: ===

Software

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

* Software Flaws and Malware
* Insecurity in Software
* Operating Systems and Security

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

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

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

# Why is software as important to security as crypto, access control, protocols?
# If your software is subject to attack, can your security can be broken Regardless of strength of crypto, access control, or protocols? Why?
# What are the main factors of software that compromise the security of systems?

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

# As above

MSc: Advanced Statistics

2023-05-21T11:45:11Z

N.zlatanov: /* Minimum Requirements For Passing The Course */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* CSE329 - Empirical Methods

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are four requirements for passing this course:
# You must attend all labs.
# You must submit all lab reports.
# You must have at least 50% on the Final Exam.
# You must have at least 50% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz during each lecture (weekly evaluations) || 15
|-
| Labs classes (weekly evaluations) || 15
|-
| Midterm || 20
|-
| Final exam || 50
|}

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures
* Ask questions and provide answers to the questions during the onsite lectures.
* Attend all of the labs and submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly questions and weekly labs

=== Final assessment ===

The final assessment is in a written form. You mast have at least 51% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in oral form.

MSc: Advanced Statistics

2023-05-21T11:44:55Z

N.zlatanov: /* Course grading range */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* CSE329 - Empirical Methods

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 85-100 || -
|-
| B. Good || 70-84 || -
|-
| C. Satisfactory || 50-69 || -
|-
| D. Poor || 0-49 || -
|}

=== Minimum Requirements For Passing The Course ===

There are four requirements for passing this course:
# You must attend all labs.
# You must submit all lab reports.
# You must have at least 51% on the Final Exam.
# You must have at least 51% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz during each lecture (weekly evaluations) || 15
|-
| Labs classes (weekly evaluations) || 15
|-
| Midterm || 20
|-
| Final exam || 50
|}

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures
* Ask questions and provide answers to the questions during the onsite lectures.
* Attend all of the labs and submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly questions and weekly labs

=== Final assessment ===

The final assessment is in a written form. You mast have at least 51% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in oral form.

MSc: Advanced Statistics

2023-01-22T15:21:28Z

N.zlatanov: /* Teaching Methodology: Methods, techniques, & activities */

= Advanced Statistics =
* '''Course name''': Advanced Statistics
* '''Code discipline''': DS-03
* '''Subject area''':

== Short Description ==
This course in advanced statistics with a view toward applications in data sciences. It is intended for masters students who are looking to expand their knowledge of theoretical methods used in modern research in data sciences. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. It teaches basic theoretical skills for the analysis of these objects, which include concentration inequalities, covering and packing arguments, decoupling and symmetrization tricks, chaining and comparison techniques for stochastic processes, combinatorial reasoning based on the VC dimension, and a lot more. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

== Prerequisites ==

=== Prerequisite subjects ===
* CSE329 - Empirical Methods

=== Prerequisite topics ===

== Course Topics ==
{| class="wikitable"
|+ Course Sections and Topics
|-
! Section !! Topics within the section
|-
| Concentration of sums of independent random variables ||
# Hoeffding’s inequality
# Chernoff ’s inequality
# Sub-gaussian distributions
# Sub-exponential distributions
|-
| Random vectors in high dimensions ||
# Concentration of the norm
# Covariance matrices and principal component analysis
|-
| Random matrices ||
# Nets, covering numbers and packing numbers
# Covariance estimation and clustering
|}

== Intended Learning Outcomes (ILOs) ==

=== What is the main purpose of this course? ===
The main purpose of this course is to present the fundamentals of high-dimensional statistics with applications to data science. The course presents some of the key probabilistic methods and results that may form an essential mathematical toolbox for a data scientist. This course places particular emphasis on random vectors, random matrices, and random projections. This course integrates theory with applications for covariance estimation, semidefinite programming, networks, elements of statistical learning, error correcting codes, clustering, matrix completion, dimension reduction, sparse signal recovery, sparse regression, and more.

=== 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 ...
* Explain the difference between low-dimensional and high-dimensional data
* Explain concentration inequalities and their application
* Remember the main statistical properties of high-dimensional vectors and matrices

==== 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 basic Monte Carlo computations, such as Monte Carlo integration
* Obtain simple but accurate bounds of complex statistical metrics
* Apply the median of means estimator
* Investigate simple statistics of social networks
* Exploit the thin-shell phenomenon when analysing data
* Apply data clustering and dimension reduction

==== 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 understand the problems related to statistical analysis of data.
* To apply theoretical statistics in real-life via computer simulations and thereby confirm or reject the correctness of the theoretical concepts.
* To identify the correct statistical methods that needs to be applied to data in order to solve the given tasks in real-life.
* To be able to generate and run experiments on random data samples.

== Grading ==

=== Course grading range ===
{| class="wikitable"
|+
|-
! Grade !! Range !! Description of performance
|-
| A. Excellent || 90-100 || -
|-
| B. Good || 75-89 || -
|-
| C. Satisfactory || 51-74 || -
|-
| D. Poor || 0-50 || -
|}

=== Minimum Requirements For Passing The Course ===

There are four requirements for passing this course:
# You must attend all labs.
# You must submit all lab reports.
# You must have at least 51% on the Final Exam.
# You must have at least 51% of the overall grade.

=== Course activities and grading breakdown ===
{| class="wikitable"
|+
|-
! Activity Type !! Percentage of the overall course grade
|-
| Quiz during each lecture (weekly evaluations) || 15
|-
| Labs classes (weekly evaluations) || 15
|-
| Midterm || 20
|-
| Final exam || 50
|}

=== Recommendations for students on how to succeed in the course ===
* Watch the video lecture and read the lecture notes before coming to the onsite lectures and to the labs.
* Attend the onsite lectures
* Ask questions and provide answers to the questions during the onsite lectures.
* Attend all of the labs and submit all of the lab reports.
* Prepare seriously for the midterm exam.
* Prepare seriously for the final exam.

== Resources, literature and reference materials ==

=== Open access resources ===
* The lecture notes and the video lectures provided via Moodle are sufficient for passing this course with grade A.

=== Software and tools used within the course ===
* You can use any software by your choice to perform the lab tasks.

= Teaching Methodology: Methods, techniques, & activities =

== Formative Assessment and Course Activities ==

=== Ongoing performance assessment ===
The performance will be assessed via weekly questions and weekly labs

=== Final assessment ===

The final assessment is in a written form. You mast have at least 51% on the final exam to pass the course.

=== The retake exam ===
The retake of the exam will be in oral form.