This course focuses on the use of probabilistic graphical models to represent complex domains using probability distributions. Using probabilistic graphical models to model large collections of random variables with complex interactions. Students will learn the key formalisms and main techniques in building probabilistic graphical models. And, how to use them to make predictions and support decision-making under uncertainty. Bayesian networks, directed and undirected graphical models, as well as their temporal extensions will be covered. Students will be introduced to causation and how it can be modeled. (Prerequisites: MATH302, MATH328, DATS301)

View the course schedule to find out details about each course including prerequisites, course objectives, course materials, a snapshot of the syllabi, and session dates.