Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.
Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.
Explains the evaluation of information retrieval models, including recall, precision, F-Measure, and the precision/recall tradeoff.
Covers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.
Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.
Covers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.
Introduces the basics of text data analysis, covering document retrieval, classification, sentiment analysis, and topic detection using preprocessing techniques and machine learning models.
Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.
