Information Retrieval BasicsIntroduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.
Information retrieval: vector spaceCovers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.
Information Retrieval BasicsIntroduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.
Probabilistic RetrievalCovers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.
Latent Semantic IndexingCovers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.
Latent Semantic IndexingCovers Latent Semantic Indexing, a method to improve information retrieval by mapping documents and queries into a lower-dimensional concept space.
Probabilistic Retrieval ModelsCovers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.
Handling Text: Document Retrieval, Classification, Sentiment AnalysisExplores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.
