STAT 535

Statistical Learning: Modeling, Prediction, and Computing
3.0 credits

Course Description

Covers statistical learning over discrete multivariate domains, exemplified by graphical probability models. Emphasizes the algorithmic and computational aspects of these models. Includes additional topics in probability and statistics of discrete structures, general purpose discrete optimization algorithms like dynamic programming and minimum spanning tree, and applications to data analysis. Prerequisite: experience with programming in a high level language. Offered: A.

Current Time Schedule

Autumn, 2022

SLN/Section Time Location Instructor
21830 A
Open

T Th 12:30PM - 1:50PM

CMU 226

Marina Meila

STUDENTS WILL NEED STRONG MATH/STAT BACKGROUND