Topics in supervised learning, unsupervised learning, dimension reduction, and feature selection. Course covers multiple methods (e.g., regression, tree-based models, and deep-learning models) and their implementation in the R computing environment. An emphasis is placed on rigorously training and testing models to achieve high reliability and accuracy.