COMP 588 Probabilistic Graphical Models

4 credits · Computer Science · Faculty of Science · Not scheduled for 2026–2027

Representation, inference and learning with graphical models; directed and undirected graphical models; exact inference; approximate inference using deterministic optimization based methods, stochastic sampling based methods; learning with complete and partial observations.

Prerequisites

COMP 251, MATH 323, MATH 324; or equivalents.

Prerequisite courses: COMP 251, MATH 323, MATH 324

Restrictions

Not open to students who have taken COMP 766 or COMP 767 when the topic was "Probabilistic Graphical Models". A background in AI (COMP 424) and machine learning (COMP 451 or COMP 551) is highly recommended.

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