STAT 5P87 - Computational Statistics

Classification: logistic regression, linear and quadratic discriminant analysis. Resampling methods: cross-validation and bootstrap. Linear model selection and regularization: subset selection, shrinkage methods, dimension reduction methods, considerations in high dimensions. Nonlinear regression: polynomial regression, regression splines, smoothing splines, local regression and generalized additive models. Tree-based methods: decision trees, bagging, random forests, and boosting. Support vector machines: maximal margin classifier, support vector classifier, support vector machines (SVMs), SVMs with more than two classes. Unsupervised learning: principal component analysis, clustering methods.

Prerequisite(s): STAT 3P82 and STAT 3P86 (or their equivalences), or permission of the instructor.