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 and Boolean retrieval, vector space retrieval, and similarity computation.
Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.
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.
Introduces the basics of text data analysis, covering document retrieval, classification, sentiment analysis, and topic detection using preprocessing techniques and machine learning models.
Covers the basic concepts of text-based information retrieval and how documents are indexed and retrieved based on user queries.
