Explores document classification methods, including k-Nearest-Neighbors, Naïve Bayes Classifier, transformer models, and multi-head attention.
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 Naive Bayes classifier, covering independence assumptions, conditional probabilities, and applications in document classification and medical diagnosis.
