ASRM 451. Basics of Statistical Learning (Same as STAT 432)
Topics in supervised and unsupervised learning are covered, including logistic regression, support vector machines, classification trees and nonparametric regression. Model building and feature selection are discussed for these techniques, with a focus on regularization methods, such as lasso and ridge regression, as well as methods for model selection and assessment using cross validation. Cluster analysis and principal components analysis are introduced as examples of unsupervised learning.
Credit: 3 undergraduate hours. 4 graduate hours.
Prerequisite: STAT 400, and either STAT 420 or STAT 425.