Topics include but are not limited to supervised learning methods: linear model, generalized linear model, logistic regression, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), nearest neighbor classifier, support vector machines, tree-based methods (decision tree, random forest, XGBoost), and neural networks; and unsupervised learning methods: clustering, principal component analysis, and independent component analysis. The course provides an extended introduction to tools widely used for statistical machine learning. Basic programming skills are preferred. Prerequisite: MATH 3330.