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.
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
|Term||Class Number||Campus||Instruction Mode||Instructor||Section||Session||Schedule||Enrollment||Location||Credits||Grading Basis||Notes|
|1213 16082 1 001||Spring 2021||16082||Storrs||Distance Learning||Nabavi, Sheida||001||Reg||TuTh 12:30pm‑1:45pm
||0/20||No Room Required - Online||3.00||Graded|