Lecture 1 - Introduction to the course
Lecture 2 - Introduction to Information Retrieval
Lecture 3 - Text Processing: Term Selection
Lecture 4 - Text Processing: Term Filtering and Linguistic Processing
Lecture 5 - Indexing
Lecture 6 - Inverted Index
Lecture 7 - Making of the Inverted Index
Lecture 8 - Storing Inverted Index
Lecture 9 - Inverted Index Summary
Lecture 10 - Statistical Properties of Text - Zipf's Law
Lecture 11 - Statistical Properties of Text - Heap's Law
Lecture 12 - Index Compression
Lecture 13 - Getting started with PyLucene
Lecture 14 - Installing Docker and PyLucene
Lecture 15 - Understanding Different Fields in PyLucene - 1
Lecture 16 - Understanding Different Fields in PyLucene - 2
Lecture 17 - Reading a Small Toy Dataset for Indexing using PyLucene
Lecture 18 - Different Analyzers in PyLucene
Lecture 19 - Indexing using PyLucene - Setting the Parameters
Lecture 20 - Indexing using PyLucene - 2
Lecture 21 - Non-English Text Analysis using PyLucene
Lecture 22 - Exploring Luke: The Lucene Index Viewer
Lecture 23 - Experimenting with Different Options in Indexing
Lecture 24 - PyLucene Practice Programs - 1
Lecture 25 - PyLucene Practice Programs - 2
Lecture 26 - Query Processing
Lecture 27 - Introduction to Ranked Retrieval
Lecture 28 - Introduction to Term Weightings for Retrieval - TF
Lecture 29 - Introduction to Term Weightings for Retrieval - IDF
Lecture 30 - Vector Space Model - 1
Lecture 31 - Vector Space Model - 2
Lecture 32 - Vector Space Model - 3
Lecture 33 - Vector Space Model - Problem Solving - 1
Lecture 34 - Vector Space Model - Problem Solving - 2
Lecture 35 - Probabilistic Model - Introduction
Lecture 36 - Binary Independence Model (BIM)
Lecture 37 - Best Match Model, BM1, BM11, BM15, BM25
Lecture 38 - Dissecting BM25, BM25 vs VSM, BM25 for long queries, BM25F, BM25+
Lecture 39 - BM25, After 30 years
Lecture 40 - Introduction to Language Model for Information Retrieval
Lecture 41 - Introducing Unigram Language Model
Lecture 42 - Estimating Document Language Model
Lecture 43 - Zero Frequency Problem and introduction to Smoothing
Lecture 44 - Jelinek-Mercer and Dirichlet Smoothed Language Model
Lecture 45 - Comparing Smoothing with IDF and Summary of LM based Retrieval
Lecture 46 - Using Divergence Measures for Document Scoring
Lecture 47 - PyLucene for Retrieval
Lecture 48 - PyLucene - Varius Query Classes 1 - TermQuery
Lecture 49 - PyLucene - Varius Query Classes 2 - PhraseQuery, TermRangeQuery, Numerical Range Query, PrefixQuery
Lecture 50 - PyLucene - Varius Query Classes 3 - BooleanQuery, WildcardQuery, FuzzyQuery, MatchAllDocsQuery
Lecture 51 - Evaluating Information Retrieval Systems 1 - Set Based Evaluation Metrics
Lecture 52 - Evaluating Information Retrieval Systems 2 - Precision@K, R-Prec, Incorporating Ranking in Precision and Recall
Lecture 53 - Evaluating Information Retrieval Systems 3 - AP, MAP, GMAP, MRR
Lecture 54 - Evaluating Information Retrieval Systems 4 - Graded relevance, nDCG
Lecture 55 - Evaluating Information Retrieval Systems 5 - Hypothesis Testing, Role of Evaluation Forums, Kappa measure
Lecture 56 - Indexing in Benchmark Dataset - Indexing TREC-like Benchmark Datasets
Lecture 57 - Retrieval in Benchmark Dataset - Retrieval in TREC-like Benchmark Datasets, and Evaluation using TREC_EVAL
Lecture 58 - Comparing Retrieval Models - Hypothesis Testing in Information Retrieval
Lecture 59 - Relevance Feedback 1 - An Introduction
Lecture 60 - Relevance Feedback 2 - Rocchio's Algorithm
Lecture 61 - Relevance Feedback 3 - Incorporating Pseudo Feedback using Relevance based Language Model, RM3
Lecture 62 - Web Search And Crawler
Lecture 63 - PageRank Algorithm
Lecture 64 - PageRank - Random Surfer's Algorithm
Lecture 65 - HITS Algorithm: Hubs and Authorities
Lecture 66 - Search Engine Optimization
Lecture 67 - Learning to Rank for Information Retrieval
Lecture 68 - Latent Semantic Indexing
Lecture 69 - Introduction to Embeddings for Information Retrieval
Lecture 70 - From Embeddings to Transformers
Lecture 71 - Large Language Models for Information Retrieval
