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.

5819. Introduction to Machine Learning

3.00 credits

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
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Term Class Number Campus Instruction Mode Instructor Section Session Schedule Enrollment Location Credits Grading Basis Notes
Fall 2020 16627 Storrs Distance Learning Miao, Fei 001 Reg TuTh 12:30pm‑1:45pm
17/10 No Room Required - Online 3.00 Graded
Spring 2021 16076 Storrs Distance Learning Yang, Qian 001 Reg TuTh 2:00pm‑3:15pm
0/10 No Room Required - Online 3.00 Graded