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 Tue, 2:00 PM at MEB 3126

Teaching Assistants

Office hours Office hours location
Joe Davison Friday 10am 3105 MEB
Gurunath Parasaram Thursday 11am 3105 MEB
Zhichao Xu Monday 10am 3105 MEB
Yuan Zhuang Wednesday 10am Online (See canvas for link)
Shashank Balija N/A N/A

Discussion forum: We will be using Canvas. Please use canvas messages and 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).