Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.
Covers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.
Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.
Covers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.
Covers Probabilistic Information Retrieval, including Query Likelihood Model, Language Modeling, and smoothing techniques for non-occurring terms.
Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.
Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.
Covers the basic concepts of text-based information retrieval and how documents are indexed and retrieved based on user queries.
Introduces the basics of information retrieval, covering text-based retrieval, document features, similarity functions, and the difference between Boolean and ranked retrieval.
