Syllabus

• Introduction.
• Queries and Documents. Models of Information retrieval. The Boolean model. The Vector model.
• Document preprocessing. Tokenization. Stemming. The Porter algorithm. Storing, indexing and searching text. Inverted indexes.
• Word distributions. The Zipf distribution. The Benford distribution. Heap's law. TF*IDF. Vector space similarity and ranking.
• Retrieval evaluation. Precision and Recall. F-measure. Reference collections. The TREC conferences.
• Automated indexing/labeling. Compression and coding. Optimal codes.
• String matching. Approximate matching.
• Query expansion. Relevance feedback.
• Text classification. Naive Bayes. Feature selection. Decision trees.
• Linear classifiers. k-nearest neighbors. Perceptron. Kernel methods. Maximum-margin classifiers. Support vector machines. Semi-supervised learning.
• Lexical semantics and Wordnet.
• Latent semantic indexing. Singular value decomposition.
• Vector space clustering. k-means clustering. EM clustering.
• Random graph models. Properties of random graphs: clustering coefficient, betweenness, diameter, giant connected component, degree distribution.
• Social network analysis. Small worlds and scale-free networks. Power law distributions. Centrality.
• Graph-based methods. Harmonic functions. Random walks.
• PageRank. Hubs and authorities. Bipartite graphs. HITS.
• Models of the Web.
• Crawling the web. Webometrics. Measuring the size of the web. The Bow-tie-method.
• Hypertext retrieval. Web-based IR. Document closures. Focused crawling.
• Question answering
• Burstiness. Self-triggerability
• Information extraction
• Adversarial IR.
• Human behavior on the web.
• Text summarization

Other possible topics

• Discovering communities, spectral clustering
• Semi-supervised retrieval
• Natural language processing. XML retrieval. Text tiling. Human behavior on the web.