COMP 588 Probabilistic Graphical Models (4 credits)

Main navigation

Minerva Class Scheduling Visit for course dates & times.

Derni猫res mises 脿 jour en lien avec la COVID-19 disponibles ici.
Latest information about COVID-19 available here.

COMP 588 Probabilistic Graphical Models (4 credits)

Note: This is the 2021鈥�2022 eCalendar. Update the year in your browser's URL bar for the most recent version of this page, or .

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.