CS 6782: Probabilistic Graphical Models Cross-Listing: BTRY 6790 (parent) 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. Offered: Fall only Prerequisites: Probability theory (BTRY 4080 or equivalent), programming and data structures (CS 2110 or equivalent); a course in statistical methods is recommended but not required (BTRY 4090 or equivalent). Grade options: Letter or S/U Credit hours: 4 Recent offerings:
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