This course provides an introduction to regression analysis and statistical learning with an emphasis on mathematical understanding and its software implementation. Programming uses Python, R, or Julia. Covered topics include the following. Linear regression: parameter estimation, confidence ellipsoids and prediction intervals, hypothesis tests. Classification: logistic regression, linear discriminant analysis. Basis expansion: polynomial regression, regression splines. Resampling methods: cross-validation, bootstrap. Shrinkage methods. Model selection: information criteria, forward and backward selection, lasso. Decision trees and random forests: bagging, boosting. Prerequisite: MATH 290 or MATH 291, and MATH 526 or MATH 628.