CS468 Information Retrieval

• 3-0-0-4-4

• Brief introduction to the field of Information Retrieval (IR).

• Explanation of the importance of IR in various domains, including search engines, digital libraries, and data mining.

Week 1:

• Lecture 1: Overview; Foundations of Information Retrieval; Source: CS54701 Information Retrieval by Clifton

• Lecture 2: Indexing and Querying; Boolean Retrieval; Source: Chapter 1 & 2, Manning, Raghavan and Schütze

• Self-Study 1: Web Crawling

Week 2:

• Self-Study 2: Indexing and Searching using pySolr

Week 3:

• Lecture 3: Text Encoding; Source: Chapter 2, Manning, Raghavan and Schütze

• Lecture 4: Dictionaries and Tolerant Retrieval; Source: Chapter 3, Manning, Raghavan and Schütze

• Self-Study 3: Boolean Retrieval using Python

Week 4:

• Lecture 5: Index Construction; Source: Chapter 4, Manning, Raghavan and Schütze

• Lecture 6: Index Compression; Source: Chapter 5, Manning, Raghavan and Schütze

• Self-Study 4: Vector Space Retrieval using Python

Week 5:

• Lecture 7: Scoring, Term weighting; Vector Space Model; Source: Chapter 6, Manning, Raghavan and Schütze

Week 6:

• Lecture 8: Computing Scores; Source: Chapter 7, Manning, Raghavan and Schütze

• Lecture 9: Evaluation in Information Retrieval; Source: Chapter 8, Manning, Raghavan and Schütze

Week 7:

• Midsem

Week 8:

• Lecture 10: Relevance Feedback and Query Expansion; Source: Chapter 9, Manning, Raghavan and Schütze

Week 9:

• Lecture 11: XML Retrieval; Source: Chapter 10, Manning, Raghavan and Schütze

• Self-Study 5: Probabilistic Retrieval using Python

Week 10:

• Lecture 12: Probabilistic Information Retrieval; Source: Chapter 11, Manning, Raghavan and Schütze

Week 11:

• Lecture 13: Language Models; Source: Chapter 12, Manning, Raghavan and Schütze

• Self-Study 6: Classification in Retrieval using Python

Week 12:

• Lecture 14: Text Classification; Source: Chapter 13, Manning, Raghavan and Schütze

Week 13:

• Lecture 15: Distributed Representations; Source: Chapter 14, Manning, Raghavan and Schütze

Week 14:

• Lecture 16: Learning Ranking; Source: Chapter 15, Manning, Raghavan and Schütze

• Project Presentation

Week 15:

• Endsem

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)

Project: Problem Formulation (10%), Midterm Exam (30%), Project: Presentation (10%), Project: Implementation (20%), Endterm Exam (30%)

Guidelines

• The project aims to conduct research on contemporary topics in Information Retrieval (IR), with a strong preference for research publications.

• Collaboration is encouraged, with groups limited to a maximum of four students. Groups are required to email their composition, project title, and an abstract before the midsem.

• It is recommended that projects include a dedicated web page.

• A comprehensive report detailing the project's objectives, methodology, findings, and conclusions must be submitted before the end of the semester. You can find the latex template and detailed guidelines for report preparation here.

• A presentation will be scheduled before the endsem, during which projects will be evaluated based on criteria including the quality of work, originality, clarity of presentation, and the content of the report. You can find the presentation template here.

• Any form of plagiarism will be subject to strict penalties.