This course explores methodological techniques and computational skills for modern predictive analytics and machine learning. Course topics include linear and generalized linear regression methods, binary and multiclass classification algorithms, and clustering and dimension reduction techniques. Students will also be introduced to important issues regarding interpretability and flexibility, variable selection and regularization, model assessment and explainable artificial intelligence. This course provides practical applications of predictive modeling with contemporary programming tools. Prerequisite: STAT 707 (F;S;SS).