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: 09-AUG-22 05.20.17.316132 AM
| Term | Class Number | Campus | Instruction Mode | Instructor | Section | Session | Schedule | Enrollment | Location | Credits | Grading Basis | Notes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1223 16590 1 001 | Spring 2022 | 16590 | Storrs | Distance Learning | Miao, Fei | 001 | Reg | MoWe 6:00pm‑7:15pm |
27/25 | No Room Required - Online | 3.00 | Graded | |
| 1228 9468 1 001 | Fall 2022 | 9468 | Storrs | In Person | He, Suining | 001 | Reg | TuTh 11:00am‑12:15pm |
18/20 | MCHU 306 | 3.00 | Graded | |
| 1228 17150 1 002 | Fall 2022 | 17150 | Storrs | In Person | Miao, Fei | 002 | Reg | MoWe 2:15pm‑3:20pm |
0/30 | ITE 336 | 3.00 | Graded |