MATH 4481 - Statistical Learning I

Prerequisite: MATH 2228. This course is designed to introduce various topics in statistical learning, with a focus on supervised learning models, specifically linear models. Topics include linear and logistic regression; linear discriminant analysis; cross-validation and bootstrap resampling methods; and model selection. Statistical computations will be performed using software such as R, Python, or MATLAB, thus prior programming experience is expected. 3 credits.