STAT 617 - Multivariate Methods and Statistical Learning

Credit(s): 3

MULTIVARIATE METHODS
Component: Lecture
Random vectors and matrices, multivariate analysis of variance and covariance; classification and discrimination (linear and quadratic discriminant analysis); principal components analysis; resampling methods including cross-validation and bootstrap; support vector machines; classification and regression trees; random forests; boosting; cluster analysis; supervised dimension reduction.
Repeatable for Credit: N Allowed Units: 3 Multiple Term Enrollment: N Grading Basis: Student Option
PREREQ: STAT 602.