A modern course in multivariate statistics. Elements of classical multivariate analysis as needed, including multivariate normal and Wishart distributions. Clustering; principal component analysis. Model selection and evaluation; prediction error; variable selection; stepwise regression; regularized regression. Cross-validation. Classification; linear discriminant analysis. Tree-based methods. Time permitting, optional topics may include nonparametric density estimation, multivariate regression, support vector machines, and random forests.
Prerequisite: CSE 131, Math 233, (Math 309 or Math 429), (Math 493 or Math 3211), (Math 494 or Math 4211), Math 439. Prior knowledge of R at the level introduces in Math 439 is assumed.
Course Attributes: FA NSM; AR NSM; AS NSM