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

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Zaid Harchaoui

GRADE OF AT LEAST 3.0 IN STAT 535 OR CSE 546 IS REQUIRED. OFFERED VIA REMOTE LEARNING