Information Retrieval Course [Prof. Ashish Sureka, Monsoon 2017, Ashoka University]

1. Vector Space Model

1.1. Term Frequency

1.2. TF-IDF Weighting

1.3. Dot Product

1.4. Vector Scores

2. Evaluation in IR

2.1. Test Collections

2.2. Precision and Recall

2.3. Mean Average Precision (MAP)

2.4. Assessing Relevance

3. Text Classification

3.1. Naive Bayes Classifier

3.2. Feature Selection

3.3. Classifier Evaluation

4. Clustering

4.1. Clustering Evaluation

4.2. Cardinality

4.3. K-Means

5. Web Search Basics

5.1. Web Graph

5.2. User Query Needs

5.3. Index Size and Estimation

6. Boolean Retrieval

6.1. IR Problem

6.2. Inverted Index

6.3. Boolean Queries

6.4. Ranked Retrieval

7. Term Vocabulary & Postings Lists

7.1. Tokenization

7.2. Stemming

7.3. Normalization

7.4. Positional Indexes

7.5. Skip Pointers

8. Vector Space Classification

8.1. K-Nearest Neighbor

8.2. Non Linear Classifier

8.3. Bias-Variance Tradeoff

9. Latent Semantic Indexing

9.1. Matrix Decompositions

9.2. SVD

9.3. Low Rank Approximations

10. Link Analysis

10.1. Page Rank

10.2. Hubs and Authorities

10.3. Markov Chains