STAT 681

Introduction to Statistical Learning

Introduction to the interface between statistical theory and modern data analytic techniques beginning with an overview of supervised and unsupervised learning and continuing with an in depth look at model assessment, selection, and regularization, and the statistical theory underlying data analytic methods such as smoothing, penalized least squares, resampling plans, classification, tree methods, random forests, bagging, boosting, and support vector machines. Practical problems are solved using statistical software packages. A particular emphasis is placed on high dimensional problems

Units: 3.0