MATH 454 - Statistical Learning

This course is targeted at statisticians and FE practitioners who wish to use cutting-edge statistical learning techniques to analyze their data. The main goal of the topic is to provide a toolset to deal with vast and complex data that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. The class presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include principal component analysis, linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, clustering, and Bayesian MCMC modeling. The lecture also enhances the ability of using the programming software R.
Units: 4
Course Type: Seminar