Stat 760 Elements of Statistical Learning

Statistical learning or machine learning methodology explores various ways of estimating functional dependencies between a response variable and possibly a large set of explanatory variables (features), when one is trying to find and understand an unknown, regular component within the realm of noisy, complex data. Modern regression and pattern recognition analyses fall in this framework. This course will provide an overview of supervised learning and discussions of statistical learning algorithms such as Discriminant Analysis, Classification Tree, Support Vector Machines, and Boosting, and illustrate practical uses of the algorithms.
Winter 2008 course webpage

Stat 763 Nonparametric Function Estimation

Statistics 763 aims to introduce nonparametric function estimation method with roughness penalty. Starting from smoothing splines for univariate data, a unified framework is developed for flexible model building of multivariate data with both Gaussian and non-Gaussian responses. Mathematical formulation of smoothing splines, reproducing kernel Hilbert space methods, selection of smoothing parameter, computation, and their applications are treated in detail.
Spring 2008 course webpage

Statistics 881 aims to provide an introduction to statistical learning theory. It focuses on formulation of prediction problems, in particular, classification in a probabilistic framework and how to estimate and analyze the performance of statistical and computational learning methods. Concepts and techniques for the theoretical analysis of such methods will be developed. Topics include notion of consistency, concentration inequalities, uniform convergence, empirical risk minimization, convex optimization, and general treatment of kernel methods and boosting among others.
Spring 2008 course webpage

Statistics 529 is the second course in a three quarter sequence in data analysis. It covers parametric and non-parametric two-sample procedures, simple linear regression analysis, and one-way analysis of variance.
Winter 2008 course webpage

Statistics 528 is the first course in a three quarter sequence in data analysis. The main focus is to introduce descriptive statistics, concepts of the probability and distributions, and statistical inference.