Meetings & Staff
Lectures: Tue & Thu, 9:10 AM – 10:30 AM.
Instructor: Vivek Srikumar
|Office hours||Tue 10:45 AM|
|Jadie Adams||jadie at sci dot utah dot edu||Wed, 9:30 AM - 10:30 AM|
|Ashim Gupta||ashim at cs dot utah dot edu||Mon, 1:30 PM - 2:30 PM|
|Mattia Medina-Grespan||mattiamg at cs dot utah dot edu||Thu, 10:45 AM - 11:45 AM|
Meeting URLs: All the URLs for class and office hours are available on Canvas.
See Canvas for the URLs for the class and office hours.
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, and neural networks.
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).