Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.
Covers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.
Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.
Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.
Covers the basics of Python programming, focusing on flow control structures, interactive mode, script mode, and variables.
Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.
Introduces the basics of information retrieval, covering text-based retrieval, document features, similarity functions, and the difference between Boolean and ranked retrieval.
