CSxxx Fundamentals of Information Retrieval

• Course Objectives

  • This course introduces the fundamental concepts, models, architectures, and evaluation techniques of Information Retrieval (IR). Students will learn how search engines work, understand the core retrieval models, and gain hands-on knowledge of indexing, ranking, and evaluation of retrieval systems.

• Learning Outcomes

  • LO1: Learn to write code for text indexing and retrieval

  • LO2: Learn to evaluate information retrieval systems

  • LO3: Learn to analyze textual and semi-structured data sets

  • LO4: Learn about text similarity measure

  • LO5: Understanding about search engine

Unit 1: Introduction of Information Retrieval

• Introduction to Information Retrieval, History and Applications of IR, Information Retrieval vs Database Systems, Components of an IR System, Document Collections and Corpora, Text Preprocessing (Tokenization, Stopword Removal, Stemming and Lemmatization), Query Processing, Overview of Search Engines.

Unit 2: Indexing and Retrieval Models

• Dictionary and Inverted Index, Positional Indexes, Efficient Index Construction, Boolean Retrieval Model, Vector Space Model, TF-IDF Weighting, Cosine Similarity, Probabilistic Retrieval, BM25, Introduction to Language Modeling for Information Retrieval.

Unit 3: Search System Implementation

• Query Processing Pipeline, Ranked Retrieval, Relevance Feedback, Basic Query Expansion, Web Crawling Fundamentals, Search Engine Architecture, Basic Retrieval Optimization.

Unit 4: Evaluation of Information Retrieval Systems

• Evaluation Methodology, Precision, Recall, F1-score, Precision@K, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), NDCG, Test Collections, TREC Benchmark, Introduction to User-Centric Evaluation.

• Lecture 0: Course Introduction.

• Lecture 1: Introduction to Information Retrieval.

• Lecture 2: Web Crawling Fundamentals. Quiz 1

• Self-Study 1: Web Crawling

• Lecture 3: Text Preprocessing; Introduction to Indexing. Quiz 2

• Lecture 4: Efficient Index Construction. Quiz 3

• Self-Study 2: Indexing and Searching using pySolr

• Lecture 5: Boolean Retrieval Model. Quiz 4

• Lecture 6: Vector Space Model, TF-IDF Weighting, Cosine Similarity. Quiz 5

• Self-Study 3: Boolean Retrieval using Python

• Lecture 7: Probabilistic Retrieval, BM25. Quiz 6

• Lecture 8: Language Modeling for Information Retrieval. Quiz 7

• Self-Study 4: Vector Space Retrieval using Python

• Lecture 9: Query Processing Pipeline, Ranked Retrieval. Quiz 8

• Lecture 10: Relevance Feedback, Basic Query Expansion. Quiz 9

• Self-Study 5: Probabilistic Retrieval using Python

• Lecture 11: Basic Retrieval Optimization. Quiz 10

• Lecture 12: Evaluation Methodology, Precision, Recall, F1-score, Precision@K, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR). Quiz 11

• Self-Study 6: Basic Query Expansion using Python

• Lecture 13: Evaluation Methodology, NDCG, Test Collections. Quiz 12

• Lecture 14: TREC Benchmark, Introduction to User-Centric Evaluation.

• Self-Study 7: IR System Evaluation using Python

Introduction to Information Retrieval, by C. Manning, P. Raghavan, and H. Schütze (Cambridge University Press, 2008).

Search Engines: Information Retrieval in Practice. Croft, W. Bruce; Metzler, Donald; Strohman, Trevor. Addison Wesley (2008)

Information Retrieval: Implementing and Evaluating Search Engines, Stefan Buettcher, Charles L. A. Clarke, Gordon V. Cormack. MIT Press. (2010)

Modern Information Retrieval, Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Addison-Wesley, (1999)

• 12 Quizzes: 24%

• 1 Assignment: 20%

• Group Project: 40% (Implementation 10%, Knowledge 10%, Analysis 20%)

• Attendance: 16%