STAT 441 - Statistical Methods for Learning and Data Mining

3 units (fi 6)(EITHER, 3-0-0)

Review of linear and nonlinear regression and brief introduction to generalized linear models, the course covers selected methods of dimension reduction (principal components, factor analysis, canonical correlations), of unsupervised (clustering, multidimensional scaling ordination) and supervised classification (discriminant analysis, logistic regression, nearest neighbours - including, among others, the machine learning methods like classification trees, neural networks, and support vector machines). Prerequisite: STAT 378.

STAT 513 - Statistical Computing

3 units (fi 6)(EITHER, 3-0-0)

Introduction to contemporary computational culture: reproducible coding, literate programming. Monte Carlo methods: random number generation, variance reduction, numerical integration, statistical simulations. Optimization (linear search, gradient descent, Newton-Raphson, method of scoring, and their specifics in the statistical context), EM algorithm. Fundamentals of convex optimization with constraints. Prerequisites: consent of the instructor.