STA 295-01 - Special Topic: Introduction to Statistical Learning

4 credits (Spring)

This course is an overview of modern approaches to analyzing and modeling large multivariate data sets across a variety of fields. Theory and implementation for common predictive techniques will be covered, including linear, penalized, and logistic regression, tree-based models, and ensemble models. Framework for model assessment, including the bias-variance trade-off, train-testing splits, and resampling methods, will be discussed. This course will make extensive use of the R programming language.

Prerequisite: STA 209 with grades S, C, or better.
Instructor: W. Rebelsky