Martha White, PhD

Associate Professor, Faculty of Science - Computing Science


Associate Professor, Faculty of Science - Computing Science


CMPUT 267 - Basics of Machine Learning

This course introduces the fundamental statistical, mathematical, and computational concepts in analyzing data. The goal for this introductory course is to provide a solid foundation in the mathematics of machine learning, in preparation for more advanced machine learning concepts. The learning outcomes are to become more comfortable with underlying concepts in machine learning, including how to formalize learning problems using probability and statistics; how models can be estimated from data; what sound estimation principles look like; how generalization is achieved; and how to evaluate the performance of learned models. Specific topics include: basic probability and optimization concepts, maximum likelihood, linear regression and polynomial regression, classification with logistic regression and regularization. Prerequisites: CMPUT 174 or 274; one of MATH 100, 114, 117, 134, 144, or 154. Corequisites: CMPUT 175 or 275; CMPUT 272; MATH 125 or 127; one of STAT 141, 151, 235, or 265, or SCI 151.

Fall Term 2021 Winter Term 2022
CMPUT 396 - Topics in Computing Science

This topics course is designed for new course offerings that may be offered in a given term. Prerequisites are determined by the instructor in the course outline. See Note (3) above.

Fall Term 2021
CMPUT 605 - Topics in Computing Science

Fall Term 2021

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