Ivan Mizera

Professor, Faculty of Science - Mathematics & Statistical Sciences


Professor, Faculty of Science - Mathematics & Statistical Sciences






STAT 413 - Introduction to Computing for Data Science

Survey of contemporary languages/environments suitable for algorithms of Statistics and Data Science. Introduction to Monte Carlo methods, random number generation and numerical integration in statistical context and optimization for both smooth and constrained alternatives, tailored to specific applications in statistics and machine learning. Prerequisites: STAT 265 or consent of the instructor.

Winter Term 2021
STAT 441 - Statistical Methods for Learning and Data Mining

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.

Winter Term 2021
STAT 513 - Statistical Computing

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.

Winter Term 2021
STAT 575 - Multivariate Analysis

The multivariate normal distribution, multivariate regression and analysis of variance, classification, canonical correlation, principal components, factor analysis. Prerequisite: STAT 372 and STAT 512.

Winter Term 2021

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