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.

Course Website

Course Website 

Current Time Schedule

Autumn, 2021

SLN/Section Time Location Instructor
21676 A
Open

T T 12:30PM - 1:50PM

CMU 226

Marina Meila

STAT FEE BASED MS STUDENTS ARE REQUIRED TO HAVE AT LEAST 3.0 IN 534 (OR EQUIVALENT ABILITY)STAT535 IS FOR THE SECOND YEAR MS STUDENTS.