Difference between revisions of "BSc: Information Retrieval"

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= Information Retrieval =
  
= Information Retrieval =
 
* '''Course name''': Information Retrieval
  
* '''Course name''': Information Retrieval
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* '''Code discipline''': XYZ
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* '''Code discipline''': CSE306
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* '''Subject area''': Data Science; Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web
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* '''Subject area''': Data Science; Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web; Recommender systems
   
 
== Short Description ==
  
== Short Description ==
  +
The course gives an introduction to practical and theoretical aspects of information search and recommender systems.
−
This course covers the following concepts: Indexing; Relevance; Ranking; Information retrieval; Query.
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This course covers the following concepts: Indexing; Search quality assessment; Relevance; Ranking; Information retrieval; Query; Recommendations; Multimedia retrieval.
   
 
== Prerequisites ==
  
== Prerequisites ==
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# Content-based image retrieval
  
# Content-based image retrieval
 
# Audio retrieval
  
# Audio retrieval
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# Hum to search
  
 
# Relevance feedback
  
# Relevance feedback
 
|}
 
|}
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! Grade !! Range !! Description of performance
  
! Grade !! Range !! Description of performance
 
|-
  
|-
−
| A. Excellent || 84-100 || -
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| A. Excellent || 90-100 || -
 
|-
  
|-
−
| B. Good || 72-83 || -
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| B. Good || 75-89 || -
 
|-
  
|-
−
| C. Satisfactory || 60-71 || -
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| C. Satisfactory || 60-74 || -
 
|-
  
|-
 
| 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 || 35
 +
| Assignments || 60
 
|-
  
|-
  +
| Quizzes || 40
−
| Interim performance assessment || 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 ==
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* Baeza-Yates, Ribeiro-Neto, Modern Information Retrieval, 2011, Addison-Wesley
  
* Baeza-Yates, Ribeiro-Neto, Modern Information Retrieval, 2011, Addison-Wesley
 
* Buttcher, Clarke, Cormack, Information Retrieval: Implementing and Evaluating Search Engines, 2010, MIT Press
  
* Buttcher, Clarke, Cormack, Information Retrieval: Implementing and Evaluating Search Engines, 2010, MIT Press
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* Course repository in github.
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* [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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=== 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 12:46, 16 September 2023

Information Retrieval

  • Course name: Information Retrieval
  • Code discipline: CSE306
  • Subject area: Data Science; Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web; Recommender systems

Short Description

The course gives an introduction to practical and theoretical aspects of information search and recommender systems. This course covers the following concepts: Indexing; Search quality assessment; Relevance; Ranking; Information retrieval; Query; Recommendations; Multimedia retrieval.

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. 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 90-100 -
B. Good 75-89 -
C. Satisfactory 60-74 -
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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