- Nov 17: Homework 6 released.
- Nov 2: Homework 5 released.
- Oct 19: Homework 4 released.
- Sep 27: Homework 3 released.
- Sep 13: Homework 2 released.
Meetings & Staff
Lectures: Tue & Thu, 9:10 AM – 10:30 AM at WEB L101
Instructor: Vivek Srikumar
|Office hours||Tue 10:45 AM (after class), 3126 MEB|
||Mon, 4:30 – 5:30 PM|
||Wed, 3:00 – 4:00 PM|
||Mon, 1:30 – 2:30 PM|
||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:
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).