Multivariate Analysis and Data Mining

Catalog description

Extensions of exploratory data analysis and modeling to multivariate data. Multivariate normal distribution and its properties. Principal component analysis and unsupervised learning such as clustering methods. Shrinkage methods for regression models including ridge regression and LASSO. Various supervised machine learning models such as discriminant analysis, neural network, support vector machine, and random forest. Offered as STAT 326 and STAT 426 .