CS60092: Information Retrieval
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| Course name | Information Retrieval | ||||||||||||||||||||||||||||
| Offered by | Computer Science & Engineering | ||||||||||||||||||||||||||||
| Credits | 3 | ||||||||||||||||||||||||||||
| L-T-P | 3-0-0 | ||||||||||||||||||||||||||||
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| Semester | Spring | ||||||||||||||||||||||||||||
Syllabus[edit | edit source]
Syllabus mentioned in ERP[edit | edit source]
Introduction to Information Retrieval: The nature of unstructured and semistructured text. Inverted index and Boolean queries.Text Indexing, Storage and Compression: Text encoding: tokenization, stemming, stop words, phrases, index optimization. Index compression: lexicon compression and postings. lists compression. Gap encoding, gamma codes, Zipfs Law. Index construction. Postings size estimation, merge sort, dynamic indexing, positional indexes, n-gram indexes, real-world issues.Retrieval Models: Boolean, vector space, TFIDF, Okapi, probabilistic, language modeling, latent semantic indexing. Vector space scoring. The cosine measure. Efficiency considerations. Document length normalization. Relevance feedback and query expansion. Rocchio.Performance Evaluation: Evaluating search engines. User happiness, precision, recall, Fmeasure. Creating test collections: kappa measure, interjudge agreement.Text Categorization and Filtering: Introduction to text classification. Naive Bayes models. Spam filtering. Vector space classification using hyperplanes; centroids; k Nearest Neighbors. Support vector machine classifiers. Kernel functions. Boosting.Text Clustering: Clustering versus classification. Partitioning methods. k-means clustering. Mixture of Gaussians model. Hierarchical agglomerative clustering. Clustering terms using documents.Advanced Topics: Summarization, Topic detection and tracking, Personalization, Question answering, Cross language information retrievalWeb Information Retrieval: Hypertext, web crawling, search engines, ranking, link analysis, PageRank, HITS, XML and Semantic web.References1.Manning, Raghavan and Schutze, Introduction to Information Retrieval, Cambridge University Press.2.Baeza-Yates and Ribeiro-Neto, Modern Information Retrieval, AddisonWesley.3.Soumen Charabarti, Mining the Web, Morgan-Kaufmann.4.Survey by Ed Greengrass available in the Internet.
Concepts taught in class[edit | edit source]
Student Opinion[edit | edit source]
How to Crack the Paper[edit | edit source]
Classroom resources[edit | edit source]
Additional Resources[edit | edit source]
Time Table[edit | edit source]
| Day | 8:00-8:55 am | 9:00-9:55 am | 10:00-10:55 am | 11:00-11:55 am | 12:00-12:55 pm | 2:00-2:55 pm | 3:00-3:55 pm | 4:00-4:55 pm | 5:00-5:55 pm | |
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| Thursday | CSE-120 | CSE-120 | ||||||||
| Friday | CSE-120 |