Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.
Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.
Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.
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 information retrieval, covering text-based retrieval, document features, similarity functions, and the difference between Boolean and ranked retrieval.
Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.
Covers the basic concepts of text-based information retrieval and how documents are indexed and retrieved based on user queries.
Covers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.
Covers Latent Semantic Indexing, a method to improve information retrieval by mapping documents and queries into a lower-dimensional concept space.
Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.
