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CS 583 - Probabilistic Graphical Models

Course Description: 

This course will cover probabilistic graphical models -- powerful and interpretable models for reasoning under uncertainty. The generic families of models such as directed, undirected, and factor graphs as well as specific representations such as hidden Markov models and conditional random fields will be discussed. The discussions will include both the theoretical aspects of representation, learning, and inference, and their applications in many interesting fields such as computer vision, natural language processing, computational biology, and medical diagnosis.

Credit: 

(3-0-3)

Prerequisite: 

None

Corequisite: 

None

Attachments: