Computational Statistics

Undergraduate

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: polynominal 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 classifiers, support vector machines (SVMs), SVMs with more than two classes; Unsupervised learning: principal component analysis, clustering methods. Course Format: Lectures, 3 hours per week; Lab, 1 hour per week Restrictions: Open to Data Sciences and Analytics majors only Prerequisite(s): One of STAT 3P82 and STAT 3P86 or STAT 3P87 (DASA) or permission of the instructor