Meetings & Staff

Lectures: Tue & Thu, 9:10 AM – 10:30 AM at WEB L101

Instructor: Vivek Srikumar

Office 3126 MEB
Email svivek at cs dot utah dot edu
Office hours Tue 4:00 PM, 3126 MEB

Teaching Assistants

Email Office hours
Amy Eisenmenger u1209324 at utah dot edu Thu 1:30 PM, 3515 MEB
Giorgi Kvernadze giorgi at cs dot utah dot edu Mon 2:00 PM, 3159 MEB
Mattia Medina-Grespan mattia dot grespan at utah dot edu Mon 1:00 PM, 3159 MEB

Discussion forum: We will be using 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, clustering and dimensionality reduction.

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).