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. Recommended preparation: MATH 2210Q; STAT 3025, or 3345, or 3375, or MATH 3160; CSE 3500.
Grading Basis: Graded
An introduction to the basic tools and techniques of machine learning, including models for both supervised and unsupervised learning, related optimization techniques, and methods for model validation. Topics include linear and logistic regression, SVM classification and regression, kernels, regularization, clustering, and on-line algorithms for regret minimization.
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|
|1208 16627 1 001||Fall 2020||16627||Storrs||Distance Learning||Miao, Fei||001||Reg||TuTh 12:30pm‑1:45pm
||17/10||No Room Required - Online||3.00||Graded|
|1213 16076 1 001||Spring 2021||16076||Storrs||Distance Learning||Yang, Qian||001||Reg||TuTh 2:00pm‑3:15pm
||0/10||No Room Required - Online||3.00||Graded|