HuskyScheduler
Open the scheduler
CSE 5830: Probabilistic Graphical Models
Probabilistic graphical models provide a flexible framework for analyzing large, complex, heterogeneous, and noisy data. They are the basis for state-of-the-art analysis methods in a wide variety of application domains, from autonomous robotics and computer vision to medical diagnosis and social networks. This course covers (a) representation, including Bayesian and Markov networks, (b) inference, both exact and approximate, and (c) estimation of both parameters and structure of graphical models.
- Credits
- 3
- Offered
- Fall 2026
- Campuses
- Storrs
- Requirements
- Department consent required; open to graduate students in the Computer Science and Engineering program, others with permission.
Fall 2026 sections
Taught by Sheida Nabavi.
| Section | Type | Meets | Instructor | Campus | Room | Seats |
|---|---|---|---|---|---|---|
| 001 | LEC | 2:30PM - 3:45PM / MoWe | Sheida Nabavi | Storrs | CHM T309 | 15 of 20 open |
Seats as of Nov 12, 2026. HuskyScheduler shows live seats when you search.
More courses
All CSE courses (Computer Science and Engineering) ยท All UConn subjects