Difference between revisions of "MSc: Advanced Information Retrieval"

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= Advanced Information Retrieval =
  
= 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 ==
−
* <span>'''Course name:'''</span> Advanced Information Retrieval
  
  +
This course covers the following concepts: Data indexing; Recommendations; Relevance and ranking.
−
* <span>'''Course number:'''</span> N/A
  
−  
−
== Course Characteristics ==
  
−  
−
== What subject area does your course (discipline) belong to? ==
  
−  
−
Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web
  
−  
−
=== Key concepts of the class ===
  
−  
−
* Data indexing
  
−
* Recommendations
  
−
* Relevance and ranking
  
−  
−
=== What is the purpose of this course? ===
  
−  
−
The course is designed to prepare students to understand and learn contemporary tools of information retrieval systems. Students, who will later dedicate their engineering or scientific careers to implementation of search engines, social networks, recommender systems and other content services will obtain necessary knowledge and skills in designing and implementing essential parts of such systems.
  
   
 
== Prerequisites ==
  
== Prerequisites ==
   
  +
=== Prerequisite subjects ===
−
The course will benefit if students already know some topics of mathematics and programming.
 
  +
* CSE101 — Introduction to Programming I
−
* Programming: python, numpy library and basic programming for internet skills (HTTP requests, networking).
  
  +
* CSE102 — Introduction to Programming II
−
* Mathematics:
 
  +
* CSE202 — Analytical Geometry and Linear Algebra I
−
Algebra: matrix multiplication, matrix decomposition (SVD, ALS) and approximation (matrix norm), sparse matrix, stability of solution (decomposition), vector spaces, metric spaces, manifold, eigenvector and eigenvalue. Discrete maths: graphs, trees, binary trees, balanced trees, metric (proximity) graphs, diameter, clique, path, shortest path. ProbStat: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.
 
  +
* 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.
−
* Analysis: DFT, [discrete] gradient.
  
  +
* 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 ===
−
How can students fill the gap?
  
−
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.
  
−
Kick start your numpy with the official quickstart guide.
  
   
   
  +
== Course Topics ==
  +
{| class="wikitable"
  +
|+ Course Sections and Topics
  +
|-
  +
! Section !! Topics within the section
  +
|-
  +
| Information retrieval basics ||
  +
# Introduction to IR, major concepts.
  +
# Crawling and Web.
  +
# Quality assessment.
  +
|-
  +
| 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 ||
  +
# Vector model
  +
# Machine learning for vector embedding
  +
# Vector-based index structures
  +
|-
  +
| Advanced topics. Media processing ||
  +
# Image and video processing, understanding and indexing
  +
# Content-based image retrieval
  +
# Audio retrieval
  +
# Relevance feedback
  +
|}
  +
== Intended Learning Outcomes (ILOs) ==
   
−
=== Course objectives based on Bloom’s taxonomy ===
 +
=== 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.
   
−
=== - What should a student remember at the end of the course? ===
 +
=== ILOs defined at three levels ===
−  
−
By the end of the course, the students should be able to remember and recognize
  
   
  +
==== 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,
  
* Terms and definitions used in area of information retrieval,
 
* Search engine and recommender system essential parts,
  
* Search engine and recommender system essential parts,
Line 52: Line 65:
 
* Indexing strategies.
  
* Indexing strategies.
   
−
=== - What should a student be able to understand at the end of the course? ===
 +
==== 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,
−
By the end of the course, the students should be able to describe and explain
  
−  
 
* How to design a recommender system from scratch,
  
* How to 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 61: Line 73:
 
* How to identify and fix information retrieval system problems.
  
* How to identify and fix information retrieval system problems.
   
−
=== - What should a student be able to apply at the end of the course? ===
 +
==== 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,
  
* 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.
  +
== Grading ==
   
−
=== Course evaluation ===
 +
=== Course grading range ===
  +
{| class="wikitable"
−  
−
{|
 +
|+
−
|+ Course grade breakdown
  
−
!align="center"|
  
−
!align="center"|
  
−
!align="center"| '''Proposed points'''
  
 
|-
  
|-
  +
! Grade !! Range !! Description of performance
−
|align="center"| Labs/seminar classes
  
−
|align="center"| 20
  
−
|align="center"| 30
  
 
|-
  
|-
  +
| A. Excellent || 84-100 || -
−
|align="center"| Interim performance assessment
  
−
|align="center"| 30
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| B. Good || 72-83 || -
−
|align="center"| Assessments (homework)
  
−
|align="center"| 0
  
−
|align="center"| 70
  
 
|-
  
|-
  +
| C. Satisfactory || 60-71 || -
−
|align="center"| Exams
  
  +
|-
−
|align="center"| 50
  
  +
| D. Poor || 0-59 || -
−
|align="center"| 0
  
 
|}
  
|}
   
  +
=== Course activities and grading breakdown ===
−
7 hometasks will cost you up to 70 points in total (10 points each). 7 contest labs can bring you up to 5 points each. Work in teams up to 3, you will get +2 points for each successful completion and +3 points for each submission in top 3.
  
  +
{| class="wikitable"
−  
  +
|+
−
=== Exam and retake planning ===
  
−  
−
'''Exam'''
  
−  
−
No exam.
  
−  
−
'''Retake 1'''
  
−  
−
First retake is conducted in a form of project defense. Student is given a week to prepare. Student takes any technical paper from Information Retrieval Journal (https://www.springer.com/journal/10791) for '''the last 3 years''' and approves it until the next day with a professor to avoid collisions and misunderstanding. Student implements the paper in a search engine (this can be a technique, metric, ...). At the retake day student presents a paper. Presentation is followed by QA session. After QA session student presents implementation of the paper. Grading criteria as follows:
  
−  
−
* 30% – paper presentation is clear, discussion of results is full.
  
−
* 30% – search engine implementation is correct and clear. Well-structured and dedicated to a separate service.
  
−
* 30% – paper implementation is correct.
  
−  
−
'''Retake 2'''
  
−  
−
Second retake is conducted in front of the committee. Four (4) questions are randomly selected for a student: two (2) theoretical from &quot;Test questions for final assessment in this section&quot; and two (2) practical from &quot;Typical questions for ongoing performance evaluation&quot;. Each question costs 25% of the grade. Student is given 15 minutes to prepare for theoretical questions. Then (s)he answers in front of the committee. After this student if given additional 40 minutes to solve practical questions.
  
−  
−
=== Grades range ===
  
−  
−
{|
  
−
|+ Course grading range
  
−
!align="center"|
  
−
!align="center"|
  
−
!align="center"| '''Proposed range'''
  
 
|-
  
|-
  +
! Activity Type !! Percentage of the overall course grade
−
|align="center"| A. Excellent
  
−
|align="center"| 90-100
  
−
|align="center"| 84-100
  
 
|-
  
|-
  +
| Assignments || 60
−
|align="center"| B. Good
  
−
|align="center"| 75-89
  
−
|align="center"| 70-83
  
 
|-
  
|-
  +
| Quizzes || 40
−
|align="center"| C. Satisfactory
  
−
|align="center"| 60-74
  
−
|align="center"| 60-69
  
 
|-
  
|-
  +
| Exams || 0
−
|align="center"| D. Poor
  
−
|align="center"| 0-59
  
−
|align="center"| 0-59
  
 
|}
  
|}
   
  +
=== Recommendations for students on how to succeed in the course ===
−
=== Resources and reference material ===
  
   
  +
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.
−
Main textbook:
  
   
  +
== Resources, literature and reference materials ==
−
* &quot;An Introduction to Information Retrieval&quot; by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Cambridge University Press (any edition)
  
   
  +
=== Open access resources ===
−
Other reference material:
  
  +
* 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
  +
* [https://github.com/IUCVLab/information-retrieval Course repository in github].
   
  +
=== Closed access resources ===
−
* “Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit,” Steven Bird, Ewan Klein, and Edward Loper. [https://www.nltk.org/book/ [link]]
  
   
−
== Course Sections ==
  
   
  +
=== Software and tools used within the course ===
−
The main sections of the course and approximate hour distribution between them is as follows:
  
   
  +
= Teaching Methodology: Methods, techniques, & activities =
−
{|
  
  +
−
|+ Course Sections
  
  +
== Activities and Teaching Methods ==
−
!align="center"| '''Section'''
  
  +
{| class="wikitable"
−
! '''Section Title'''
  
  +
|+ Activities within each section
−
!align="center"| '''Teaching Hours'''
  
 
|-
  
|-
  +
! Learning Activities !! Section 1 !! Section 2 !! Section 3 !! Section 4
−
|align="center"| 1
  
−
| Introduction. Crawling and quality basics
  
−
|align="center"| 16
  
 
|-
  
|-
  +
| Development of individual parts of software product code || 1 || 1 || 1 || 1
−
|align="center"| 2
  
−
| Text indexing and language processing
  
−
|align="center"| 20
  
 
|-
  
|-
  +
| Homework and group projects || 1 || 1 || 1 || 1
−
|align="center"| 3
  
−
| Advanced index data structures
  
−
|align="center"| 8
  
 
|-
  
|-
  +
| Testing (written or computer based) || 1 || 1 || 1 || 1
−
|align="center"| 4
  
  +
|}
−
| Advanced retrieval topics. Media retrieval
  
  +
== Formative Assessment and Course Activities ==
−
|align="center"| 16
  
−
|}
  
−  
−
=== Section 1 ===
  
−  
−
==== Section title: ====
  
−  
−
Introduction. Crawling and quality basics
  
−  
−
=== Topics covered in this section: ===
  
−  
−
* Introduction to information retrieval
  
−
* Crawling
  
−
* Quality assessment
  
   
−
=== What forms of evaluation were used to test students’ performance in this section? ===
 +
=== Ongoing performance assessment ===
   
  +
==== Section 1 ====
−
{|
  
  +
{| class="wikitable"
−
!align="center"|
  
  +
|+
−
!align="center"| '''Yes/No'''
  
 
|-
  
|-
  +
! Activity Type !! Content !! Is Graded?
−
|align="center"| Development of individual parts of software product code
  
−
|align="center"| 1
  
 
|-
  
|-
  +
| Question || Enumerate limitations for web crawling. || 1
−
|align="center"| Homework and group projects
  
−
|align="center"| 1
  
 
|-
  
|-
  +
| Question || Propose a strategy for A/B testing. || 1
−
|align="center"| Midterm evaluation
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Propose recommender quality metric. || 1
−
|align="center"| Testing (written or computer based)
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Implement DCG metric. || 1
−
|align="center"| Reports
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Discuss relevance metric. || 1
−
|align="center"| Essays
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Crawl website with respect to robots.txt. || 1
−
|align="center"| Oral polls
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || What is typical IR system architecture? || 0
−
|align="center"| Discussions
  
−
|align="center"| 0
  
−
|}
  
−  
−
=== Typical questions for ongoing performance evaluation within this section ===
  
−  
−
# Enumerate limitations for web crawling.
  
−
# Propose a strategy for A/B testing.
  
−
# Propose recommender quality metric.
  
−
# Implement DCG metric.
  
−
# Discuss relevance metric.
  
−
# Crawl website with respect to robots.txt.
  
−  
−
==== Typical questions for seminar classes (labs) within this section ====
  
−  
−
# Show how to parse a dynamic web page.
  
−
# Provide a framework to accept/reject A/B testing results.
  
−
# Compute DCG for an example query for random search engine.
  
−
# Implement a metric for a recommender system.
  
−
# Implement pFound.
  
−  
−
==== Test questions for final assessment in this section ====
  
−  
−
# Implement text crawler for a news site.
  
−
# What is SBS (side-by-side) and how is it used in search engines?
  
−
# Compare pFound with CTR and with DCG.
  
−
# Explain how A/B testing works.
  
−  
−
=== Section 2 ===
  
−  
−
==== Section title: ====
  
−  
−
Text indexing and language processing
  
−  
−
=== Topics covered in this section: ===
  
−  
−
* Building inverted index for text documents. Boolean retrieval model.
  
−
* Language, tokenization, stemming, searching, scoring.
  
−
* Spellchecking.
  
−
* Language model. Topic model.
  
−
* Vector model for texts.
  
−
* ML for text embedding.
  
−  
−
=== What forms of evaluation were used to test students’ performance in this section? ===
  
−  
−
{|
  
−
!align="center"|
  
−
!align="center"| '''Yes/No'''
  
 
|-
  
|-
  +
| Question || Show how to parse a dynamic web page. || 0
−
|align="center"| Development of individual parts of software product code
  
−
|align="center"| 1
  
 
|-
  
|-
  +
| Question || Provide a framework to accept/reject A/B testing results. || 0
−
|align="center"| Homework and group projects
  
−
|align="center"| 1
  
 
|-
  
|-
  +
| Question || Compute DCG for an example query for random search engine. || 0
−
|align="center"| Midterm evaluation
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Implement a metric for a recommender system. || 0
−
|align="center"| Testing (written or computer based)
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Implement pFound. || 0
−
|align="center"| Reports
  
  +
|}
−
|align="center"| 0
  
  +
==== Section 2 ====
  +
{| class="wikitable"
  +
|+
 
|-
  
|-
  +
! Activity Type !! Content !! Is Graded?
−
|align="center"| Essays
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Build inverted index for a text. || 1
−
|align="center"| Oral polls
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Tokenize a text. || 1
−
|align="center"| Discussions
  
−
|align="center"| 0
  
−
|}
  
−  
−
=== Typical questions for ongoing performance evaluation within this section ===
  
−  
−
# Build inverted index for a text.
  
−
# Tokenize a text.
  
−
# Implement simple spellchecker.
  
−
# Embed the text with a model.
  
−  
−
=== Typical questions for seminar classes (labs) within this section ===
  
−  
−
# Build inverted index for a set of web pages.
  
−
# build a distribution of stems/lexemes for a text.
  
−
# Choose and implement persistent index for a given text collection.
  
−
# Visualize a dataset for text classification.
  
−  
−
=== Test questions for final assessment in this section ===
  
−  
−
# Explain how (and why) KD-trees work.
  
−
# What are weak places of inverted index?
  
−
# Compare different text vectorization approaches.
  
−
# Compare tolerant retrieval to spellchecking.
  
−  
−
=== Section 3 ===
  
−  
−
==== Section title: ====
  
−  
−
Advanced index data structures
  
−  
−
==== Topics covered in this section: ====
  
−  
−
* Vector-based tree data structures.
  
−
* Graph-based data structures. Inverted index and multi-index.
  
−  
−
=== What forms of evaluation were used to test students’ performance in this section? ===
  
−  
−
{|
  
−
!align="center"|
  
−
!align="center"| '''Yes/No'''
  
 
|-
  
|-
  +
| Question || Implement simple spellchecker. || 1
−
|align="center"| Development of individual parts of software product code
  
−
|align="center"| 1
  
 
|-
  
|-
  +
| Question || Implement wildcard search. || 1
−
|align="center"| Homework and group projects
  
−
|align="center"| 1
  
 
|-
  
|-
  +
| Question || Build inverted index for a set of web pages. || 0
−
|align="center"| Midterm evaluation
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || build a distribution of stems/lexemes for a text. || 0
−
|align="center"| Testing (written or computer based)
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Choose and implement case-insensitive index for a given text collection. || 0
−
|align="center"| Reports
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Choose and implement semantic vector-based index for a given text collection. || 0
−
|align="center"| Essays
  
  +
|}
−
|align="center"| 0
  
  +
==== Section 3 ====
  +
{| class="wikitable"
  +
|+
 
|-
  
|-
  +
! Activity Type !! Content !! Is Graded?
−
|align="center"| Oral polls
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Embed the text with an ML model. || 1
−
|align="center"| Discussions
  
−
|align="center"| 0
  
−
|}
  
−  
−
=== Typical questions for ongoing performance evaluation within this section ===
  
−  
−
# Build kd-tree index for a given dataset.
  
−
# Why kd-trees work badly in 100-dimensional environment?
  
−
# What is the difference between metric space and vector space?
  
−  
−
==== Typical questions for seminar classes (labs) within this section ====
  
−  
−
# Build (H)NSW index for a dataset.
  
−
# Compare HNSW to Annoy index.
  
−
# What are metric space index structures you know?
  
−  
−
==== Test questions for final assessment in this section ====
  
−  
−
# Compare inverted index to HNSW in terms of speed, memory consumption?
  
−
# Choose the best index for a given dataset.
  
−
# Implement range search in KD-tree.
  
−  
−
=== Section 4 ===
  
−  
−
==== Section title: ====
  
−  
−
Advanced retrieval topics. Media retrieval
  
−  
−
==== Topics covered in this section: ====
  
−  
−
* Image and video processing
  
−
* Image understanding
  
−
* Video understanding
  
−
* Audio processing
  
−
* Speech-to-text
  
−
* Relevance feedback
  
−
* PageRank
  
−  
−
=== What forms of evaluation were used to test students’ performance in this section? ===
  
−  
−
{|
  
−
!align="center"|
  
−
!align="center"| '''Yes/No'''
  
 
|-
  
|-
  +
| Question || Build term-document matrix. || 1
−
|align="center"| Development of individual parts of software product code
  
−
|align="center"| 1
  
 
|-
  
|-
  +
| Question || Build semantic index for a dataset using Annoy. || 1
−
|align="center"| Homework and group projects
  
−
|align="center"| 1
  
 
|-
  
|-
  +
| Question || Build kd-tree index for a given dataset. || 1
−
|align="center"| Midterm evaluation
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Why kd-trees work badly in 100-dimensional environment? || 1
−
|align="center"| Testing (written or computer based)
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || What is the difference between metric space and vector space? || 1
−
|align="center"| Reports
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Choose and implement persistent index for a given text collection. || 0
−
|align="center"| Essays
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Visualize a dataset for text classification. || 0
−
|align="center"| Oral polls
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Build (H)NSW index for a dataset. || 0
−
|align="center"| Discussions
  
  +
|-
−
|align="center"| 0
  
  +
| Question || Compare HNSW to Annoy index. || 0
−
|}
  
  +
|-
−  
  +
| Question || What are metric space index structures you know? || 0
−
=== Typical questions for ongoing performance evaluation within this section ===
  
  +
|}
−  
  +
==== Section 4 ====
−
# Extract semantic information from images.
  
  +
{| class="wikitable"
−
# Build an image hash.
  
  +
|+
−
# Build a spectral representation of a song.
  
  +
|-
−
# Whats is relevance feedback?
  
  +
! Activity Type !! Content !! Is Graded?
−  
  +
|-
−
==== Typical questions for seminar classes (labs) within this section ====
  
  +
| Question || Extract semantic information from images. || 1
−  
  +
|-
−
# Build a &quot;search by color&quot; feature.
  
  +
| Question || Build an image hash. || 1
−
# Extract scenes from video.
  
  +
|-
−
# Write a voice-controlled search.
  
  +
| Question || Build a spectral representation of a song. || 1
−
# Semantic search within unlabelled image dataset.
  
  +
|-
−  
  +
| Question || Whats is relevance feedback? || 1
−
==== Test questions for final assessment in this section ====
  
  +
|-
−  
  +
| 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'''
  +
# 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'''
  +
# 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'''
  +
# 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'''
 
# What are the approaches to image understanding?
  
# What are the approaches to image understanding?
 
# How to cluster a video into scenes and shots?
  
# How to cluster a video into scenes and shots?
 
# How speech-to-text technology works?
  
# How speech-to-text technology works?
 
# How to build audio fingerprints?
  
# How to build audio fingerprints?
  +
  +
=== The retake exam ===
  +
'''Section 1'''
  +
# Solve a complex coding problem similar to one of the homework or lab.
  +
'''Section 2'''
  +
# Solve a complex coding problem similar to one of the homework or lab.
  +
'''Section 3'''
  +
# Solve a complex coding problem similar to one of the homework or lab.
  +
'''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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