Meetings & Staff
Lectures: Tue & Thu, 9:10 AM – 10:30 AM at WEB L101
Instructor: Vivek Srikumar
|Office hours||Tue 4:00 PM, 3126 MEB|
|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, 3167 MEB|
|Mattia Medina-Grespan||mattia dot grespan at utah dot edu||Mon 1:00 PM, 3167 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:
A broad theoretical and practical understanding of machine learning paradigms and algorithms,
The ability to implement learning algorithms,
The ability to identify where machine learning can be applied and make the most appropriate decisions (about algorithms, models, supervision, etc).