Difference between revisions of "IU:TestPage"

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  +
= Information Retrieval =
−
= Advanced Databases =
  
−
* '''Course name''': Advanced Databases
 +
* '''Course name''': Information Retrieval
 
* '''Code discipline''': XYZ
  
* '''Code discipline''': XYZ
  +
* '''Subject area''': D; a; t; a; ; S; c; i; e; n; c; e; ;; ; C; o; m; p; u; t; e; r; ; s; y; s; t; e; m; s; ; o; r; g; a; n; i; z; a; t; i; o; n; ;; ; I; n; f; o; r; m; a; t; i; o; n; ; s; y; s; t; e; m; s; ;; ; R; e; a; l; -; t; i; m; e; ; s; y; s; t; e; m; s; ;; ; I; n; f; o; r; m; a; t; i; o; n; ; r; e; t; r; i; e; v; a; l; ;; ; W; o; r; l; d; ; W; i; d; e; ; W; e; b
−
* '''Subject area''': x; x; x
  
   
 
== Short Description ==
  
== Short Description ==
−
This course covers the following concepts: DevOps Engineering concepts:.
 +
This course covers the following concepts: Indexing; Relevance; Ranking; Information retrieval; Query.
   
 
== Prerequisites ==
  
== Prerequisites ==
   
 
=== Prerequisite subjects ===
  
=== Prerequisite subjects ===
  +
* CSE204 — Analytic Geometry And Linear Algebra II: matrix multiplication, matrix decomposition (SVD, ALS) and approximation (matrix norm), sparse matrix, stability of solution (decomposition), vector spaces, metric spaces, manifold, eigenvector and eigenvalue.
−  
  +
* CSE113 — Philosophy I - (Discrete Math and Logic): graphs, trees, binary trees, balanced trees, metric (proximity) graphs, diameter, clique, path, shortest path.
  +
* CSE206 — Probability And Statistics: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties. Analysis: DFT, [discrete] gradient.
   
 
=== Prerequisite topics ===
  
=== Prerequisite topics ===
Line 22: Line 24:
 
! Section !! Topics within the section
  
! Section !! Topics within the section
 
|-
  
|-
  +
| Information retrieval basics ||
−
| Introduction to subject, computer networks basics, transport layer protocols, and socket programming ||
 
  +
# Introduction to IR, major concepts.
−
# General introduction to the course
  
  +
# Crawling and Web.
−
# Computer networks basic
  
  +
# Quality assessment.
−
# Socket programming
  
−
# UDP socket programming
  
−
# TCP socket programming
  
 
|-
  
|-
  +
| Text processing and indexing ||
−
| ||
 
  +
# Building inverted index for text documents. Boolean retrieval model.
−  
  +
# Language, tokenization, stemming, searching, scoring.
  +
# Spellchecking and wildcard search.
  +
# Suggest and query expansion.
  +
# Language modelling. Topic modelling.
 
|-
  
|-
  +
| Vector model and vector indexing ||
−
| Coordination, consistency, and replication in distributed systems ||
 
  +
# Vector model
−  
  +
# Machine learning for vector embedding
  +
# Vector-based index structures
 
|-
  
|-
  +
| Advanced topics. Media processing ||
−
| Fault tolerance and security in distributed systems ||
 
  +
# Image and video processing, understanding and indexing
−  
  +
# Content-based image retrieval
  +
# Audio retrieval
  +
# Hum to search
  +
# Relevance feedback
 
|}
 
|}
 
== Intended Learning Outcomes (ILOs) ==
  
== Intended Learning Outcomes (ILOs) ==
   
 
=== What is the main purpose of this course? ===
  
=== What is the main purpose of this course? ===
  +
The course is designed to prepare students to understand background theories of information retrieval systems and introduce different information retrieval systems. The course will focus on the evaluation and analysis of such systems as well as how they are implemented. Throughout the course, students will be involved in discussions, readings, and assignments to experience real world systems. The technologies and algorithms covered in this class include machine learning, data mining, natural language processing, data indexing, and so on.
−
Advanced Databases have become
  
   
 
=== ILOs defined at three levels ===
  
=== ILOs defined at three levels ===
Line 47: Line 57:
 
==== Level 1: What concepts should a student know/remember/explain? ====
  
==== Level 1: What concepts should a student know/remember/explain? ====
 
By the end of the course, the students should be able to ...
  
By the end of the course, the students should be able to ...
  +
* Terms and definitions used in area of information retrieval,
−  
  +
* Search engine and recommender system essential parts,
  +
* Quality metrics of information retrieval systems,
  +
* Contemporary approaches to semantic data analysis,
  +
* Indexing strategies.
   
 
==== Level 2: What basic practical skills should a student be able to perform? ====
  
==== 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 ...
  
By the end of the course, the students should be able to ...
  +
* Understand background theories behind information retrieval systems,
−  
  +
* How to design a recommender system from scratch,
  +
* How to evaluate quality of a particular information retrieval system,
  +
* Core ideas and system implementation and maintenance,
  +
* How to identify and fix information retrieval system problems.
   
 
==== Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios? ====
  
==== 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 ...
  
By the end of the course, the students should be able to ...
  +
* Build a recommender service from scratch,
−
 
  +
* Implement a proper index for an unstructured dataset,
  +
* Plan quality measures for a new recommender service,
  +
* Run initial data analysis and problem evaluation for a business task, related to information retrieval.
 
== Grading ==
  
== Grading ==
   
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! Grade !! Range !! Description of performance
  
! Grade !! Range !! Description of performance
 
|-
  
|-
−
| A. Excellent || 90-100 || -
 +
| A. Excellent || 84-100 || -
 
|-
  
|-
−
| B. Good || 75-89 || -
 +
| B. Good || 72-83 || -
 
|-
  
|-
−
| C. Satisfactory || 60-74 || -
 +
| C. Satisfactory || 60-71 || -
 
|-
  
|-
 
| D. Poor || 0-59 || -
  
| D. Poor || 0-59 || -
Line 79: Line 100:
 
! Activity Type !! Percentage of the overall course grade
  
! Activity Type !! Percentage of the overall course grade
 
|-
  
|-
−
| Laboratory assignments || 55%
 +
| Labs/seminar classes || 35
 
|-
  
|-
  +
| Interim performance assessment || 70
−
| Final exam || 35%
  
 
|-
  
|-
−
| Attendance || 10%
 +
| Exams || 0
 
|}
  
|}
   
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=== Open access resources ===
  
=== Open access resources ===
  +
* Manning, Raghavan, Schütze, An Introduction to Information Retrieval, 2008, Cambridge University Press
−
* Textbook:. Available online:
  
  +
* Baeza-Yates, Ribeiro-Neto, Modern Information Retrieval, 2011, Addison-Wesley
−
* Reference:. Available online:
  
  +
* Buttcher, Clarke, Cormack, Information Retrieval: Implementing and Evaluating Search Engines, 2010, MIT Press
−
* Reference:. Available online: h
  
  +
* Course repository in github.
   
 
=== Closed access resources ===
  
=== Closed access resources ===
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|-
  
|-
 
| Testing (written or computer based) || 1 || 1 || 1 || 1
  
| Testing (written or computer based) || 1 || 1 || 1 || 1
−
|-
  
−
| Oral polls || 1 || 1 || 1 || 1
  
−
|-
  
−
| Discussions || 1 || 1 || 1 || 1
  
 
|}
 
|}
 
== Formative Assessment and Course Activities ==
  
== Formative Assessment and Course Activities ==
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! Activity Type !! Content !! Is Graded?
  
! Activity Type !! Content !! Is Graded?
 
|-
  
|-
−
| Question || ? || 1
 +
| Question || Enumerate limitations for web crawling. || 1
 
|-
  
|-
−
| Question || . || 1
 +
| Question || Propose a strategy for A/B testing. || 1
 
|-
  
|-
−
| Question || ? || 1
 +
| Question || Propose recommender quality metric. || 1
 
|-
  
|-
−
| Question || ? || 1
 +
| Question || Implement DCG metric. || 1
 
|-
  
|-
−
| Question || ? || 1
 +
| Question || Discuss relevance metric. || 1
 
|-
  
|-
−
| Question || ? || 1
 +
| Question || Crawl website with respect to robots.txt. || 1
 
|-
  
|-
−
| Question || ? || 1
 +
| Question || What is typical IR system architecture? || 0
  +
|-
  +
| Question || Show how to parse a dynamic web page. || 0
  +
|-
  +
| Question || Provide a framework to accept/reject A/B testing results. || 0
  +
|-
  +
| Question || Compute DCG for an example query for random search engine. || 0
  +
|-
  +
| Question || Implement a metric for a recommender system. || 0
  +
|-
  +
| Question || Implement pFound. || 0
 
|}
 
|}
 
==== Section 2 ====
  
==== Section 2 ====
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! Activity Type !! Content !! Is Graded?
  
! Activity Type !! Content !! Is Graded?
 
|-
  
|-
  +
| Question || Build inverted index for a text. || 1
−
| Question || You have a list of large numbers, and you need to find if they are prime or not. Would you use multithreading, multiprocessing, or sequential programming in order to complete the task asap? Prove it in practice. || 0
  
  +
|-
  +
| Question || Tokenize a text. || 1
  +
|-
  +
| Question || Implement simple spellchecker. || 1
  +
|-
  +
| Question || Implement wildcard search. || 1
  +
|-
  +
| Question || Build inverted index for a set of web pages. || 0
 
|-
  
|-
  +
| Question || build a distribution of stems/lexemes for a text. || 0
−
| Question || You need to send multiple requests to a server and receive responses. Assume there is a few msecs of delay before you receive the response from the server. Would you use multithreading, multiprocessing, or sequential programming in order to complete the task asap? Prove it in practice. (Order of the requests/responses doesn't matter) || 0
  
 
|-
  
|-
  +
| Question || Choose and implement case-insensitive index for a given text collection. || 0
−
| Question || Discuss two ways of creating the threads using threading module in Python: 1) passing the worker function as a target, 2) subclassing the Thread class || 0
  
 
|-
  
|-
−
| Question || Given the function implemented locally, make it available to be called through RPC from remote process? Use xmlRPC. || 0
 +
| Question || Choose and implement semantic vector-based index for a given text collection. || 0
 
|}
 
|}
 
==== Section 3 ====
  
==== Section 3 ====
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! Activity Type !! Content !! Is Graded?
  
! Activity Type !! Content !! Is Graded?
 
|-
  
|-
−
| Question || ? || 1
 +
| Question || Embed the text with an ML model. || 1
 
|-
  
|-
−
| Question || ? || 1
 +
| Question || Build term-document matrix. || 1
 
|-
  
|-
−
| Question || ? || 1
 +
| Question || Build semantic index for a dataset using Annoy. || 1
 
|-
  
|-
−
| Question || ? || 1
 +
| Question || Build kd-tree index for a given dataset. || 1
 
|-
  
|-
−
| Question || ? || 1
 +
| Question || Why kd-trees work badly in 100-dimensional environment? || 1
  +
|-
  +
| Question || What is the difference between metric space and vector space? || 1
  +
|-
  +
| Question || Choose and implement persistent index for a given text collection. || 0
  +
|-
  +
| Question || Visualize a dataset for text classification. || 0
  +
|-
  +
| Question || Build (H)NSW index for a dataset. || 0
  +
|-
  +
| Question || Compare HNSW to Annoy index. || 0
  +
|-
  +
| Question || What are metric space index structures you know? || 0
 
|}
 
|}
 
==== Section 4 ====
  
==== Section 4 ====
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! Activity Type !! Content !! Is Graded?
  
! Activity Type !! Content !! Is Graded?
 
|-
  
|-
−
| Question || Same as above || 0
 +
| Question || Extract semantic information from images. || 1
  +
|-
  +
| Question || Build an image hash. || 1
  +
|-
  +
| Question || Build a spectral representation of a song. || 1
  +
|-
  +
| Question || Whats is relevance feedback? || 1
  +
|-
  +
| Question || Build a "search by color" feature. || 0
  +
|-
  +
| Question || Extract scenes from video. || 0
  +
|-
  +
| Question || Write a voice-controlled search. || 0
  +
|-
  +
| Question || Semantic search within unlabelled image dataset. || 0
 
|}
 
|}
 
=== Final assessment ===
  
=== Final assessment ===
 
'''Section 1'''
  
'''Section 1'''
  +
# Implement text crawler for a news site.
−
# ?
  
  +
# What is SBS (side-by-side) and how is it used in search engines?
−
# ?
  
  +
# Compare pFound with CTR and with DCG.
−
# ?
  
  +
# Explain how A/B testing works.
−
# ?
  
  +
# Describe PageRank algorithm.
 
'''Section 2'''
  
'''Section 2'''
  +
# Explain how (and why) KD-trees work.
−
#
 
  +
# What are weak places of inverted index?
  +
# Compare different text vectorization approaches.
  +
# Compare tolerant retrieval to spellchecking.
 
'''Section 3'''
  
'''Section 3'''
  +
# Compare inverted index to HNSW in terms of speed, memory consumption?
−  
  +
# Choose the best index for a given dataset.
  +
# Implement range search in KD-tree.
 
'''Section 4'''
  
'''Section 4'''
  +
# What are the approaches to image understanding?
−
# Same as above
  
  +
# How to cluster a video into scenes and shots?
  +
# How speech-to-text technology works?
  +
# How to build audio fingerprints?
   
 
=== The retake exam ===
  
=== The retake exam ===

Revision as of 09:41, 12 July 2022

Information Retrieval

  • Course name: Information Retrieval
  • Code discipline: XYZ
  • Subject area: D; a; t; a;  ; S; c; i; e; n; c; e; ;;  ; C; o; m; p; u; t; e; r;  ; s; y; s; t; e; m; s;  ; o; r; g; a; n; i; z; a; t; i; o; n; ;;  ; I; n; f; o; r; m; a; t; i; o; n;  ; s; y; s; t; e; m; s; ;;  ; R; e; a; l; -; t; i; m; e;  ; s; y; s; t; e; m; s; ;;  ; I; n; f; o; r; m; a; t; i; o; n;  ; r; e; t; r; i; e; v; a; l; ;;  ; W; o; r; l; d;  ; W; i; d; e;  ; W; e; b

Short Description

This course covers the following concepts: Indexing; Relevance; Ranking; Information retrieval; Query.

Prerequisites

Prerequisite subjects

  • CSE204 — Analytic Geometry And Linear Algebra II: matrix multiplication, matrix decomposition (SVD, ALS) and approximation (matrix norm), sparse matrix, stability of solution (decomposition), vector spaces, metric spaces, manifold, eigenvector and eigenvalue.
  • CSE113 — Philosophy I - (Discrete Math and Logic): graphs, trees, binary trees, balanced trees, metric (proximity) graphs, diameter, clique, path, shortest path.
  • CSE206 — Probability And Statistics: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties. Analysis: DFT, [discrete] gradient.

Prerequisite topics

Course Topics

Course Sections and Topics
Section Topics within the section
Information retrieval basics
  1. Introduction to IR, major concepts.
  2. Crawling and Web.
  3. Quality assessment.
Text processing and indexing
  1. Building inverted index for text documents. Boolean retrieval model.
  2. Language, tokenization, stemming, searching, scoring.
  3. Spellchecking and wildcard search.
  4. Suggest and query expansion.
  5. Language modelling. Topic modelling.
Vector model and vector indexing
  1. Vector model
  2. Machine learning for vector embedding
  3. Vector-based index structures
Advanced topics. Media processing
  1. Image and video processing, understanding and indexing
  2. Content-based image retrieval
  3. Audio retrieval
  4. Hum to search
  5. Relevance feedback

Intended Learning Outcomes (ILOs)

What is the main purpose of this course?

The course is designed to prepare students to understand background theories of information retrieval systems and introduce different information retrieval systems. The course will focus on the evaluation and analysis of such systems as well as how they are implemented. Throughout the course, students will be involved in discussions, readings, and assignments to experience real world systems. The technologies and algorithms covered in this class include machine learning, data mining, natural language processing, data indexing, and so on.

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 ...

  • Terms and definitions used in area of information retrieval,
  • Search engine and recommender system essential parts,
  • Quality metrics of information retrieval systems,
  • Contemporary approaches to semantic data analysis,
  • Indexing strategies.

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 ...

  • Understand background theories behind information retrieval systems,
  • How to design a recommender system from scratch,
  • How to evaluate quality of a particular information retrieval system,
  • Core ideas and system implementation and maintenance,
  • How to identify and fix information retrieval system problems.

Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?

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

  • Build a recommender service from scratch,
  • Implement a proper index for an unstructured dataset,
  • Plan quality measures for a new recommender service,
  • Run initial data analysis and problem evaluation for a business task, related to information retrieval.

Grading

Course grading range

Grade Range Description of performance
A. Excellent 84-100 -
B. Good 72-83 -
C. Satisfactory 60-71 -
D. Poor 0-59 -

Course activities and grading breakdown

Activity Type Percentage of the overall course grade
Labs/seminar classes 35
Interim performance assessment 70
Exams 0

Recommendations for students on how to succeed in the course

Resources, literature and reference materials

Open access resources

  • Manning, Raghavan, Schütze, An Introduction to Information Retrieval, 2008, Cambridge University Press
  • Baeza-Yates, Ribeiro-Neto, Modern Information Retrieval, 2011, Addison-Wesley
  • Buttcher, Clarke, Cormack, Information Retrieval: Implementing and Evaluating Search Engines, 2010, MIT Press
  • Course repository in github.

Closed access resources

Software and tools used within the course

Teaching Methodology: Methods, techniques, & activities

Activities and Teaching Methods

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
Testing (written or computer based) 1 1 1 1

Formative Assessment and Course Activities

Ongoing performance assessment

Section 1

Activity Type Content Is Graded?
Question Enumerate limitations for web crawling. 1
Question Propose a strategy for A/B testing. 1
Question Propose recommender quality metric. 1
Question Implement DCG metric. 1
Question Discuss relevance metric. 1
Question Crawl website with respect to robots.txt. 1
Question What is typical IR system architecture? 0
Question Show how to parse a dynamic web page. 0
Question Provide a framework to accept/reject A/B testing results. 0
Question Compute DCG for an example query for random search engine. 0
Question Implement a metric for a recommender system. 0
Question Implement pFound. 0

Section 2

Activity Type Content Is Graded?
Question Build inverted index for a text. 1
Question Tokenize a text. 1
Question Implement simple spellchecker. 1
Question Implement wildcard search. 1
Question Build inverted index for a set of web pages. 0
Question build a distribution of stems/lexemes for a text. 0
Question Choose and implement case-insensitive index for a given text collection. 0
Question Choose and implement semantic vector-based index for a given text collection. 0

Section 3

Activity Type Content Is Graded?
Question Embed the text with an ML model. 1
Question Build term-document matrix. 1
Question Build semantic index for a dataset using Annoy. 1
Question Build kd-tree index for a given dataset. 1
Question Why kd-trees work badly in 100-dimensional environment? 1
Question What is the difference between metric space and vector space? 1
Question Choose and implement persistent index for a given text collection. 0
Question Visualize a dataset for text classification. 0
Question Build (H)NSW index for a dataset. 0
Question Compare HNSW to Annoy index. 0
Question What are metric space index structures you know? 0

Section 4

Activity Type Content Is Graded?
Question Extract semantic information from images. 1
Question Build an image hash. 1
Question Build a spectral representation of a song. 1
Question Whats is relevance feedback? 1
Question Build a "search by color" feature. 0
Question Extract scenes from video. 0
Question Write a voice-controlled search. 0
Question Semantic search within unlabelled image dataset. 0

Final assessment

Section 1

  1. Implement text crawler for a news site.
  2. What is SBS (side-by-side) and how is it used in search engines?
  3. Compare pFound with CTR and with DCG.
  4. Explain how A/B testing works.
  5. Describe PageRank algorithm.

Section 2

  1. Explain how (and why) KD-trees work.
  2. What are weak places of inverted index?
  3. Compare different text vectorization approaches.
  4. Compare tolerant retrieval to spellchecking.

Section 3

  1. Compare inverted index to HNSW in terms of speed, memory consumption?
  2. Choose the best index for a given dataset.
  3. Implement range search in KD-tree.

Section 4

  1. What are the approaches to image understanding?
  2. How to cluster a video into scenes and shots?
  3. How speech-to-text technology works?
  4. How to build audio fingerprints?

The retake exam

Section 1

Section 2

Section 3

Section 4

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