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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 10:45 AM (after class), 3126 MEB

Teaching Assistants:

  Email Office Hours
Jie Cao jcao at cs dot utah dot edu Mon, 4:30 – 5:30 PM
Maks Cegielski-Johnson maks.cegielski at utah dot edu Wed, 3:00 – 4:00 PM
Jiani Lin jiani at cs dot utah dot edu Mon, 1:30 – 2:30 PM
Samarth Mathur sam6191 at cs dot utah dot edu Thu, 2:00 – 3:00 PM

All TA office hours will be in MEB 3115.

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).