Graduate Course Descriptions

The following directory lists the graduate courses which the University expects to offer, although the University in no way guarantees that all such courses will be offered in any given academic year, and reserves the right to alter the list if conditions warrant. Click on the links below for a list of courses in that subject area. You may then click “View Classes” to see scheduled classes for individual courses.

5830. Probabilistic Graphical Models

3.00 credits

Prerequisites: Department consent required; open to graduate students in the Computer Science and Engineering program, others with permission.

Grading Basis: Graded

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.


Last Refreshed: 30-OCT-20 05.20.18.831626 AM
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Term Class Number Campus Instruction Mode Instructor Section Session Schedule Enrollment Location Credits Grading Basis Notes
Spring 2021 16082 Storrs Distance Learning Nabavi, Sheida 001 Reg TuTh 12:30pm‑1:45pm
0/20 No Room Required - Online 3.00 Graded