Subject
STAT - Statistics
Description
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.
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.
GFC Hours
(3-0)
Domestic Fee Rate Group
A
International Fee Rate Group
B
Signature Learning
Research & Creative Scholarship
Courses may consist of a Lecture, Lab, Tutorial, and/or Seminar. Students will be required to register in each component that is required for the course as indicated in the schedule of classes. Practicums, internships or other experiential learning modalities are typically indicated as a Lab component.
Component
LEC
Units
3
Repeat for Credit
No