Applied Multivariate Analysis and Statistical Learning Credit Hours: 3 hours The methodology of multivariate statistics and machine learning for students specializing in statistics. Topics include inference on multivariate means, multivariate analysis of variance, principal component analysis, linear discriminant analysis, factor analysis, linear discrimination, classification trees, multi-dimensional scaling, canonical correlation analysis, clustering, support vector machines, and ensemble methods. Prerequisites: Undergraduate Prerequisite: STAT 4230/6230 and (MATH 3000 or MATH 3300) Graduate Prerequisite: STAT 6420 or STAT 6220 or STAT 6315 or permission of department Level: Graduate Undergraduate
