Learning Math for Machine Learning (LMML) Reading Group

Brian Fantana: They've done studies, you know. 60% of the time it works, every time.
Ron Burgundy: That doesn't make sense.
-Anchorman (2004)


This material is now covered more systematically and professionally in a new course taught by Professor Jerry Zhu.


Machine learning research uses tools and results from a variety of mathematical fields, including (but not limited to): convex optimization, probability, statistics, functional analysis, and computational learning theory. Familiarity with these ideas is crucial in order to fully participate in many exciting research directions. The purpose of this reading group is to gain a better understanding of some mathematical foundations relevant to machine learning research. Because of this focus, much of the material covered will not be about machine learning per se, but rather about general theoretical concepts which have important applications in machine learning.

Meeting format

This group will follow the time-honored format of weekly volunteer presentations. Due to the rigorous nature of this material there will probably be little benefit to simply printing out the paper, attending the meeting, and letting the presentation wash over you in a soothing wave of lemmas and Greek symbols. Please make a serious effort to understand the readings for weeks you choose to attend. Feel free though, to attend some meetings and skip others as your schedule and interests dictate, as the content will not generally be cumulative.

Meeting schedule

Mondays at 3:00 PM, room 2310 1263 Computer Sciences
(if unavailable, backup location is CS 1289)

Mailing list

The LMML mailing list is currently inactive.

List of topics (tentative)