Difference between revisions of "MSc: Advanced Information Retrieval"

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= Advanced Information Retrieval =
  
= Advanced Information Retrieval =
 
* '''Course name''': Advanced Information Retrieval
  
* '''Course name''': Advanced Information Retrieval
−
* '''Code discipline''': N/A
 +
* '''Code discipline''': CSE334
 
* '''Subject area''': Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web
  
* '''Subject area''': Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web
   
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* CSE113 — Philosophy I - (Discrete Math and Logic): graphs, trees, binary trees, balanced trees, metric (proximity) graphs, diameter, clique, path, shortest path.
  
* 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.
  
* CSE206 — Probability And Statistics: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.
−
* Analysis: DFT, [discrete] gradient.
  
−
* 3blue1brown playlist on Linear Algebra can help to overview selected topics.
  
−
* Actually, on their channel you can find almost any maths topic, e.g. Fourier Transform.
  
−
* Gilbert Strang is one of the best human teachers of Algebra, if you prefer classic lectures to fancy videos.
  
−
* This MIT course can help you with discrete objects.
  
−
* For Russian readers there is a maths book from the course author with labs.
  
−
* Also there is a very basic python-based course on maths with lots of relevant (and irrelevant) labs.
  
−
* To have a better feeling of networking, please refer to this video lecture.
  
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* Kick start your numpy with the official quickstart guide.
  
   
 
=== Prerequisite topics ===
  
=== Prerequisite topics ===
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! Section !! Topics within the section
  
! Section !! Topics within the section
 
|-
  
|-
−
| Introduction. Crawling and quality basics ||
+
| Information retrieval basics ||
−
# Introduction to information retrieval
 +
# Introduction to IR, major concepts.
−
# Crawling
 +
# Crawling and Web.
−
# Quality assessment
 +
# Quality assessment.
 
|-
  
|-
−
| Text indexing and language processing ||
+
| Text processing and indexing ||
 
# Building inverted index for text documents. Boolean retrieval model.
  
# Building inverted index for text documents. Boolean retrieval model.
 
# Language, tokenization, stemming, searching, scoring.
  
# Language, tokenization, stemming, searching, scoring.
−
# Spellchecking.
 +
# Spellchecking and wildcard search.
  +
# Suggest and query expansion.
−
# Language model. Topic model.
 +
# Language modelling. Topic modelling.
−
# Vector model for texts.
  
⚫
# ML for text embedding.
  
 
|-
  
|-
−
| Advanced index data structures ||
+
| Vector model and vector indexing ||
−
# Vector-based tree data structures.
 +
# Vector model
  ⚫
# Machine learning for vector embedding
−
# Graph-based data structures. Inverted index and multi-index.
  
  +
# Vector-based index structures
 
|-
  
|-
−
| Advanced retrieval topics. Media retrieval ||
+
| Advanced topics. Media processing ||
−
# Image and video processing
 +
# Image and video processing, understanding and indexing
  +
# Content-based image retrieval
−
# Image understanding
  
  ⚫
# Audio retrieval
−
# Video understanding
  
⚫
# Audio processing
  
−
# Speech-to-text
  
 
# Relevance feedback
  
# Relevance feedback
−
# PageRank
  
 
|}
 
|}
 
== 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 and learn contemporary tools of information retrieval systems. Students, who will later dedicate their engineering or scientific careers to implementation of search engines, social networks, recommender systems and other content services will obtain necessary knowledge and skills in designing and implementing essential parts of such systems.
 +
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 ===
  
=== ILOs defined at three levels ===
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==== 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 design a recommender system from scratch,
 
* How to evaluate quality of a particular information retrieval system,
  
* How to evaluate quality of a particular information retrieval system,
Line 87: Line 76:
 
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,
  
* Build a recommender service from scratch,
−
* Implement proper index for an unstructured dataset,
 +
* Implement a proper index for an unstructured dataset,
 
* Plan quality measures for a new recommender service,
  
* Plan quality measures for a new recommender service,
 
* Run initial data analysis and problem evaluation for a business task, related to information retrieval.
 
* Run initial data analysis and problem evaluation for a business task, related to information retrieval.
Line 100: Line 89:
 
| A. Excellent || 84-100 || -
  
| A. Excellent || 84-100 || -
 
|-
  
|-
−
| B. Good || 70-83 || -
 +
| B. Good || 72-83 || -
 
|-
  
|-
−
| C. Satisfactory || 60-69 || -
 +
| C. Satisfactory || 60-71 || -
 
|-
  
|-
 
| D. Poor || 0-59 || -
  
| D. Poor || 0-59 || -
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! Activity Type !! Percentage of the overall course grade
  
! Activity Type !! Percentage of the overall course grade
 
|-
  
|-
−
| Labs/seminar classes || 30
 +
| Assignments || 60
 
|-
  
|-
  +
| Quizzes || 40
−
| Interim performance assessment || 0
  
⚫
|-
  
−
| Assessments (homework) || 70
  
 
|-
  
|-
 
| Exams || 0
  
| Exams || 0
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=== Recommendations for students on how to succeed in the course ===
  
=== Recommendations for students on how to succeed in the course ===
   
  +
The simples way to succeed is to participate in labs and pass coding assignments in timely manner. This guarantees up to 60% of the grade. Participation in lecture quizzes allow to differentiate the grade.
   
 
== Resources, literature and reference materials ==
  
== Resources, literature and reference materials ==
   
 
=== Open access resources ===
  
=== Open access resources ===
−
* "An Introduction to Information Retrieval" by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Cambridge University Press (any edition)
 +
* Manning, Raghavan, Schütze, An Introduction to Information Retrieval, 2008, Cambridge University Press
  +
* Baeza-Yates, Ribeiro-Neto, Modern Information Retrieval, 2011, Addison-Wesley
−
* “Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit,” Steven Bird, Ewan Klein, and Edward Loper. [link]
  
  +
* Buttcher, Clarke, Cormack, Information Retrieval: Implementing and Evaluating Search Engines, 2010, MIT Press
  +
* [https://github.com/IUCVLab/information-retrieval Course repository in github].
   
 
=== Closed access resources ===
  
=== Closed access resources ===
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=== Software and tools used within the course ===
  
=== Software and tools used within the course ===
  +
−
 
 
= Teaching Methodology: Methods, techniques, & activities =
  
= Teaching Methodology: Methods, techniques, & activities =
   
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|-
  
|-
 
| Homework and group projects || 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 ==
  
== Formative Assessment and Course Activities ==
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|-
  
|-
 
| Question || Crawl website with respect to robots.txt. || 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 || Show how to parse a dynamic web page. || 0
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| Question || Implement simple spellchecker. || 1
  
| Question || Implement simple spellchecker. || 1
 
|-
  
|-
−
| Question || Embed the text with a model. || 1
 +
| Question || Implement wildcard search. || 1
 
|-
  
|-
 
| Question || Build inverted index for a set of web pages. || 0
  
| Question || Build inverted index for a set of web pages. || 0
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| Question || build a distribution of stems/lexemes for a text. || 0
  
| Question || build a distribution of stems/lexemes for a text. || 0
 
|-
  
|-
−
| Question || Choose and implement persistent index for a given text collection. || 0
 +
| Question || Choose and implement case-insensitive index for a given text collection. || 0
 
|-
  
|-
−
| Question || Visualize a dataset for text classification. || 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 || 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 || Build kd-tree index for a given dataset. || 1
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|-
  
|-
 
| Question || What is the difference between metric space and vector space? || 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 || Build (H)NSW index for a dataset. || 0
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# Compare pFound with CTR and with DCG.
  
# Compare pFound with CTR and with DCG.
 
# Explain how A/B testing works.
  
# Explain how A/B testing works.
  +
# Describe PageRank algorithm.
 
'''Section 2'''
  
'''Section 2'''
 
# Explain how (and why) KD-trees work.
  
# Explain how (and why) KD-trees work.
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=== The retake exam ===
  
=== The retake exam ===
 
'''Section 1'''
  
'''Section 1'''
  +
# Solve a complex coding problem similar to one of the homework or lab.
−  
 
'''Section 2'''
  
'''Section 2'''
  +
# Solve a complex coding problem similar to one of the homework or lab.
−  
 
'''Section 3'''
  
'''Section 3'''
  +
# Solve a complex coding problem similar to one of the homework or lab.
−  
 
'''Section 4'''
  
'''Section 4'''
  +
# Solve a complex coding problem similar to one of the homework or lab.

Latest revision as of 17:35, 19 December 2022

Advanced Information Retrieval

  • Course name: Advanced Information Retrieval
  • Code discipline: CSE334
  • Subject area: Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web

Short Description

This course covers the following concepts: Data indexing; Recommendations; Relevance and ranking.

Prerequisites

Prerequisite subjects

  • CSE101 — Introduction to Programming I
  • CSE102 — Introduction to Programming II
  • CSE202 — Analytical Geometry and Linear Algebra I
  • 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.

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. 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
Assignments 60
Quizzes 40
Exams 0

Recommendations for students on how to succeed in the course

The simples way to succeed is to participate in labs and pass coding assignments in timely manner. This guarantees up to 60% of the grade. Participation in lecture quizzes allow to differentiate the grade.

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

  1. Solve a complex coding problem similar to one of the homework or lab.

Section 2

  1. Solve a complex coding problem similar to one of the homework or lab.

Section 3

  1. Solve a complex coding problem similar to one of the homework or lab.

Section 4

  1. Solve a complex coding problem similar to one of the homework or lab.
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