STAT 538
Statistical Learning: Modeling, Prediction, and Computing
3.0 credits
Course Description
Reviews optimization and convex optimization in its relation to statistics. Covers the basics of unconstrained and constrained convex optimization, basics of clustering and classification, entropy, KL divergence and exponential family models, duality, modern learning algorithms like boosting, support vector machines, and variational approximations in inference. Prerequisite: experience with programming in a high level language. Offered: W.
Current Time Schedule
Winter, 2021
| SLN/Section | Time | Location | Instructor |
|---|---|---|---|
|
20466 A Open |
T T 10:00AM - 11:20AM |
* * |
Zaid Harchaoui |
|
GRADE OF AT LEAST 3.0 IN STAT 535 OR CSE 546 IS REQUIRED. OFFERED VIA REMOTE LEARNING
|
|||