Meetings & Staff

Lectures: Tue & Thu, 12:25 PM – 1:45 PM, WEB L101. See lectures page for lecture video links.

Instructor: Vivek Srikumar

Email svivek at cs dot utah dot edu
Office hours Thursdays, 10am at MEB 3126

Teaching Assistants

Office hours Location
Oliver Bentham Tuesdays 10-11am 3145 MEB
Purbid Bambroo Mondays 3-4pm 3145 MEB

Discussion forum: We will be using Piazza. You can access it via canvas. Please use the discussion forum as the preferred medium for interacting with the instructor and the teaching assistants rather than emailing directly.

Course objectives, or: What can I expect to learn?

This course covers techniques for developing computer programs that can acquire new knowledge automatically or adapt their behavior over time. Topics include several algorithms for supervised and unsupervised learning, decision trees, online learning, linear classifiers, empirical risk minimization, computational learning theory, ensemble methods, Bayesian methods, and neural networks.

Expected learning outcomes: By the end of the semester, we hope that you will have:

  1. A broad theoretical and practical understanding of machine learning paradigms and algorithms,

  2. The ability to implement learning algorithms,

  3. The ability to identify where machine learning can be applied and make the most appropriate decisions (about algorithms, models, supervision, etc).