EECS 6002 Machine Learning Theory

MW 11:30am-1pm, BRG 211

Course description

This course takes a foundational perspective on machine learning and covers some of its underlying mathematical principles. Topics range from well-established results in learning theory to current research challenges. We start with introducing a formal framework, and then introduce and analyze learning methods, such as Nearest Neighbors, Boosting, SVMs and Neural Networks. Finally, students present and discuss recent research papers.

Announcements

December 4
Here are some instructions for your paper report.

November 1
We have a review session for the exam on Friday, November 3, in Bergeron 217.

October 29

October 24
The third set of exercises for practice is out. Solutions will be discussed in class on October 30.

October 2
The second set of exercises for practice is out. Solutions will be discussed in class on October 11.

September 22
The first set of exercises for practice is out. Solutions will be discussed in class on October 2.

Lectures

Exercises

Literature

Potential project papers

Fairness in Machine Learning

Understanding Deep Learning

Unsupervised Learning