COMP 588. Probabilistic Graphical Models.
Credits: 4
Offered by: Computer Science (Faculty of Science)
Terms offered: Winter 2026
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Description
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.
- 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.