Course Details
Course Code: DATS344 Course ID: 5143 Credit Hours: 3 Level: Undergraduate
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)
Course Schedule
Registration Dates | Course Dates | Start Month | Session | Weeks |
---|---|---|---|---|
Registration12/30/2024 - 05/30/2025 | Course Dates06/02/2025 - 07/27/2025 | Start Month June | SessionSpring 2025 Session D | Weeks8 Week session |
Registration03/31/2025 - 08/29/2025 | Course Dates09/01/2025 - 10/26/2025 | Start Month September | SessionSummer 2025 Session D | Weeks8 Week session |