Catalog Course Description

Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.

Summer Term

4.5 hours of Instructor presentation of course materials per week and 10.5 hours of Outside Work Hours per week and 3 hours of Instructional experiences requiring special laboratory equipment and facilities per week.

Spring Term

3 hours of Instructor presentation of course materials per week and 7 hours of Outside Work Hours per week and 2 hours of Instructional experiences requiring special laboratory equipment and facilities per week.

Fall Term

3 hours of Instructor presentation of course materials per week and 7 hours of Outside Work Hours per week and 2 hours of Instructional experiences requiring special laboratory equipment and facilities per week.