STAT 435

Introduction to Statistical Machine Learning
4.0 credits

Course Description

Introduces the theory and application of statistical machine learning. Topics may include supervised versus unsupervised learning; cross-validation; the bias-variance trade-off; regression and classification; regularization and shrinkage approaches; non-linear approaches; tree-based methods; and support vector machines. Includes applications in R. Prerequisite: either STAT 341, STAT 390/MATH 390, or STAT 391; recommended: MATH 208. Offered: Sp.

Syllabus Downloads

Upcoming Time Schedule

Spring, 2023

SLN/Section Time Location Instructor
20171 A
Open

T Th 11:30AM - 12:50PM

W 1:30PM - 2:20PM

GUG 218

MGH 231

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