COMP 588

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COMP 588 Probabilistic Graphical Models (4 credits)

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Offered by: Computer Science (Faculty of Science)

Overview

Computer Science (Sci) : 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.

Terms: Winter 2022

Instructors: Ravanbakhsh, Siamak (Winter)

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.