CMPUT 463 - Probabilistic Graphical Models

3 units (fi 6)(EITHER, 3-0-3)

Faculty of Science

Probabilistic graphical models (PGMs; including Bayesian Belief Nets, Markov Random Fields, etc.) now contribute significantly to many areas, including expert systems, computer perception (vision and speech), natural language interpretation, automated decision making, and robotics. This course provides an introduction to this field, describing semantics, inference and learning, as well as practical applications of these systems. Programming assignments will include hands-on experiments with various reasoning systems. Prerequisites: one of CMPUT 204 or CMPUT 275; and one of CMPUT 267 or CMPUT 466.