1. Lectures
1.1. Introduction
1.1.1. Overveiw
1.1.2. boolean retrieval
1.1.2.1. incedence matrix
1.1.2.2. inverted file index
1.1.2.3. query optimization
1.2. Dictionaries?
1.2.1. Vocabulary
1.2.2. Dictionaries
1.2.3. Phrase and wildcard queries
1.3. Index construction & compression
1.3.1. BSBI
1.3.2. SPIMI
1.3.3. Distributed indexing
1.3.4. Dictionary Compression
1.3.5. Posting compression
1.4. Vector space model and Term Frequancy
1.4.1. TF
1.4.2. tf-idf
1.4.3. vector space
1.5. Naive Bayes classifier (document classification?)
1.5.1. Naive Bayes
1.5.2. Evaluation
1.5.3. Assumptions
1.6. Linear classification + relevance feedback
1.6.1. Feature selection
1.6.2. Linear classifier
1.6.3. Relevance feedback
1.6.4. Query expansion
1.7. Document clustering
1.7.1. Clustering
1.7.2. K-means
1.7.3. Evaluation
1.7.4. k selection
1.8. Hyperlink Analysis
1.8.1. Anchors
1.8.2. PageRank
1.8.3. HITS
1.9. Web search
1.9.1. Web IR
1.9.2. Ads & Spam
1.9.3. Size of Web
1.10. Web search & recommender Systems
1.10.1. Size of Web
1.10.2. Duplicate detection
1.10.3. Recommender Systems
2. Tutorials
2.1. Statistics and Machine Learning Basiscs
2.1.1. Probability
2.1.2. Naive Bayes
2.1.3. Linear classifier
2.1.4. Overfitting and VC dimensions
2.2. Evaluation of classifiers
2.2.1. Holdout
2.2.2. Cross validation
2.2.3. ROC
2.3. Clustering
2.3.1. EM
2.3.2. Mean shift
2.3.3. Clustering stability
3. Projects
3.1. Boolean Model and Term Vocabulary
3.1.1. phase 1
3.1.2. phase 2
3.2. Vector Space Model and Query Expansion
3.2.1. phase 1
3.2.2. phase 2