Bailey Kacsmar

About

I am an Assistant Professor at the University of Alberta and an Alberta machine intelligence institute (Amii) fellow. I received my PhD in Computer Science and my Masters of Mathematics from the University of Waterloo.

Research

My research interests are broadly in the development and evaluation of technical privacy solutions; including the use of privacy mechanisms for data analysis. I aim to develop human-centered privacy technology through the parallel study of technical solutions for private computation (privacy in machine learning and private set intersections) alongside the corresponding user perceptions, concerns, and comprehension of these developments. My current focus is on human-centered privacy-preserving machine learning.

CMPUT 466 - Machine Learning

Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course covers a variety of learning scenarios (supervised, unsupervised and partially supervised), as well as foundational methods for regression, classification, dimensionality reduction and modeling. Techniques such as kernels, optimization and probabilistic graphical models will typically be introduced. It will also provide the formal foundations for understanding when learning is possible and practical. Credit cannot be obtained for both CMPUT 367 and CMPUT 466. Prerequisites: CMPUT 204 or 275; MATH 125; CMPUT 267 or MATH 214; or consent of the instructor.

CMPUT 499 - Topics in Computing Science

This topics course is designed for a one on one individual study course between a student and an instructor. Prerequisites are determined by the instructor in the course outline. See Note (3) above.

CMPUT 566 - Topics in Computing Science

CMPUT 605 - Topics in Computing Science

CMPUT 701 - Essay in Computing Science I

A major essay on an agreed topic.

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