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: (MATH 3300 or MATH 3300E or MATH 3000) and (MATH 2270 or MATH 2270H or MATH 2500 or MATH 2500E) and STAT 4230/6230 and (STAT 4360/6360 or STAT 4360E/6360E or STAT 4365/6365) Graduate Prerequisite: STAT 6420 or permission of department Level: Graduate Undergraduate
