Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.
Covers Latent Semantic Indexing, a method to improve information retrieval by mapping documents and queries into a lower-dimensional concept space.
Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.
Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.
Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.
Explores lexical semantics, word sense, semantic relations, and WordNet, highlighting applications in language engineering and information retrieval.
Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.
Introduces the basics of information retrieval, covering text-based retrieval, document features, similarity functions, and the difference between Boolean and ranked retrieval.
