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CSE 5819: Introduction to Machine Learning
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.
- Credits
- 3
- Offered
- Spring 2027, Fall 2026
- Campuses
- Storrs
- Requirements
- 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.
Spring 2027 sections
Taught by Suining He and Jinbo Bi.
| Section | Type | Meets | Instructor | Campus | Room | Seats |
|---|---|---|---|---|---|---|
| 001 | LEC | 12:30PM - 1:45PM / MoWe | Suining He | Storrs | MCHU 301 | 25 of 25 open |
| 002 | LEC | 12:30PM - 1:45PM / TuTh | Jinbo Bi | Storrs | MCHU 305 | 16 of 20 open |
Seats as of Nov 12, 2026. HuskyScheduler shows live seats when you search.
Fall 2026 sections
Taught by Jinbo Bi and Jonathan Ji.
| Section | Type | Meets | Instructor | Campus | Room | Seats |
|---|---|---|---|---|---|---|
| 002 | LEC | 2:00PM - 3:15PM / TuTh | Jinbo Bi | Storrs | ITE 336 | 4 of 15 open |
| 003 | LEC | 12:30PM - 1:45PM / TuTh | Jonathan Ji | Storrs | MCHU 302 | 2 of 5 open |
Seats as of Nov 12, 2026. HuskyScheduler shows live seats when you search.
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