18.408: Algorithmic Aspects of Machine Learning

Fall 2017



Modern machine learning systems are often built on top of algorithms that do not have provable guarantees, and it is the subject of debate when and why they work. In this class, we will focus on designing algorithms whose performance we can rigorously analyze for fundamental machine learning problems. We will cover topics such as: nonnegative matrix factorization, tensor decomposition, sparse coding, learning mixture models, matrix completion and inference in graphical models. Almost all of these problems are computationally hard in the worst-case and so developing an algorithmic theory is about (1) choosing the right models in which to study these problems and (2) developing the appropriate mathematical tools (often from probability, geometry or algebra) in order to rigorously analyze existing heuristics, or to design fundamentally new algorithms.

Announcement: Course evaluations are here, please take a few minutes to fill it out before it closes on Monday, December 18th at 9am!

Announcement: The final project description is posted here and due on December 13th, by email

Course Information

Problem Sets

Additional Notes

Course Outline

Here is a tentative outline for the course: