An introduction to statistical learning methods, exploring both the computational and statistical aspects of data analysis. Topics include numerical linear algebra, convex optimization techniques, basics of stochastic simulation, nonparametric methods, kernel methods, graphical models, decision trees, and data resampling. Three lecture hours a week for one semester. Statistics and Scientific Computation 386C and Statistics and Data Sciences 386C may not both be counted. Prerequisite: Graduate standing.