ST 563
Introduction to Statistical Learning
Section: 601

Course Description

This course will introduce common statistical learning methods for supervised and unsupervised predictive learning in both the regression and classification settings. Topics covered will include linear and polynomial regression, logistic regression and discriminant analysis, cross-validation and the bootstrap, model selection and regularization methods, splines and generalized additive models, principal components, hierarchical clustering, nearest neighbor, kernel, and tree-based methods, ensemble methods, boosting, and support-vector machines.

DE Program

Statistics Online Masters Program

SUM2 5W 2020

Instructors

Classes Start:
June 22, 2020
Classes End:
July 24, 2020
Distance Education:
Yes
Class Type:
Lecture
Credits:
3.00
Delivery Method:
Internet
Restrictions:
Prerequisite: ST 512 or ST 514 or ST 515 or ST 517 Requirement: NDS or Grad Students Only

Tools