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Probabalistic Graphical ModelsEdit

Probabilistic graphical modeling is taught by Professor Daphne Koller & Professor Kevin Murphy.


Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning.

External LinksEdit

Stanford PGM Class

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