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BTRY4790 Course Description

A thorough introduction to graphical models, a flexible and powerful framework for machine learning and probabilistic modeling that combines graph theory and probability theory. Covers both directed models (Bayesian networks) and undirected models, inference and parameter learning, and exact and approximate algorithms. Special cases such as hidden Markov models, tree-like Bayesian nets, and conditional random fields are discussed in detail.