Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.
Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.
Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.
Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.
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 text-based retrieval, document features, similarity functions, and the difference between Boolean and ranked retrieval.
Covers Latent Semantic Indexing, a method to improve information retrieval by mapping documents and queries into a lower-dimensional concept space.
