CO 1: Understand and describe the principles and components of information retrieval systems.
CO 2: Apply and analyze core algorithms, term weighting, and probabilistic models in information retrieval systems.
CO 3: Evaluate and synthesize advanced techniques like Latent Semantic Indexing, web search algorithms, and cross-language retrieval within information retrieval systems
CO 4: Create and evaluate information retrieval systems using test collections, user-centered approaches, and ethical considerations.
Module 1: Introduction to Information Retrieval - Information Retrieval: Definition and Importance, Real-world Applications, Challenges of Handling Large Volume of Information, Need for Efficient Retrieval Techniques, Retrieval Models: Boolean, Vector Space and Probabilistic Models, Query Processing and Document Indexing, Evaluation Matrices in Information Retrieval: Precision, Recall, F1-Score, Mean Average Precision
Module 2: Information Retrieval Algorithms - Inverted Index Construction and Compression, Term Weighting: TF-IDF, Vector Space Model and Cosine Similarity, Probabilistic Retrieval Models: Okapi BM25, Handling Queries with Multiple Terms, Relevance Feedback and Query Expansion
Module 3: Advanced Information Retrieval Techniques - Latent Semantic Indexing (LSI) and Singular Value Decomposition (SVD), Web Search and Link Analysis: PageRank, HITS algorithm, Machine Learning for Information Retrieval, Cross-language Information Retrieval, Handling Multimedia Content in Retrieval Systems.
Module 4: Evaluation and Optimization of Information Retrieval Systems - Test Collections and Evaluation Methodologies, Information Retrieval Evaluation Metrics, Performance Optimization Techniques, User-centered Evaluation and User Studies, Ethical Considerations in Information Retrieval
"Introduction to Information Retrieval" by Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze
"Modern Information Retrieval" by Ricardo Baeza-Yates and Berthier Ribeiro-Neto
"Information Retrieval: Algorithms and Data Structures" by William B. Frakes and Ricardo
"Information Retrieval: Implementing and Evaluating Search Engines" by Stefan Büttcher, Charles L. A. Clarke, and Gordon V. Cormack
Please note that the presentation materials will not be shared with the learners. The learners are requested to prepare lecture notes and also make use of the text and reference books available at the University library. Supplementary materials related to the course will be shared with the learners from time-to-time, through GitHub only.
The exercises, codes, supplementary materials, and relevant papers will be made available at this GitHub Repository.
The learners are requested to clone the repository and create a local copy of their working directory (Instructions available in the README file).
All the learners should maintain a repository of all the coding exercises and assignments in their GitHub profile which will be evaluated by the instructor from time-to-time.
There will not be any written assignments as part of this course. There will be regular quizzes conducted (both surprise and planned quizzes) during the course. Since this course uses project-based learning strategy, there will be group activities such as interim and final project presentation. Also, there will be individual viva planned to assess the progress of each learner. The instructor may decide appropriate assignments and evaluation methodologies from time-to-time.
The video lectures related to the course may be added to the Instructor's YouTube Channel available HERE. You may subscribe this channel for receiving notification when new videos are uploaded.
The instructor may share other publicly available relevant videos with the learners as and when required to supplement the learning process.
