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 CSE program, others with permission. Recommended preparation: CSE3500; MATH 2110, 3160 or STAT 3345Q, or the equivalent; CSE 4820 or 5819 are also desirable but not as critical.
Grading Basis: Graded
Bayesian machine learning is a unifying methodology for reasoning about uncertainty when modelling complex data. This course begins by covering the foundations of probabilistic modelling, Monte Carlo and variational inference algorithms, and model checking. We build on these foundations by considering essential models, e.g., mixed-membership and hierarchical models, and their applications. The course concludes with a survey of recent advances in Bayesian machine learning focusing on Bayesian nonparametrics and other advanced topics.
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 16085 1 001||Spring 2021||16085||Storrs||Distance Learning||Aguiar, Derek||001||Reg||TuTh 4:45pm‑6:00pm
||5/20||No Room Required - Online||3.00||Graded|