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).
This course may not be repeated for credit.
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.
Sections
This course will be offered next in
Winter 2025.