Statistical Learning

3 credits · Graduate Programs

Introduction and Linear Regression; Classification; Regularization; Model Assessment and Selection; Support Vector Machines; Unsupervised Learning; Tree-Based Methods; Other Topics (e.g., Neural Networks, Graphical Models, High-Dimensional Data). Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department. Antirequisite(s): Credit for Statistics 641 and 543 will not be allowed.