DS 303 - Statistical Learning

The topics that are discussed in this course are: Basic statistical learning methods such as linear regression and classification; resampling methods such as cross-validation and bootstrapping; model selection methods such as subset selection, ridge and lasso regression, and principle component analysis; tree-based methods such as decision trees and random forests; additional topics may include support vector machines and deep learning. Appropriate R packages are used for the aforementioned methods.

Credits: 4

Prerequisites/Restrictions: DS-203 or ST-201 or Permission of Instructor

Core: Quantitative Reasoning