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.
