Frank Wood


Machine learning is a broad field that spans reinforcement learning, deep learning, Bayesian nonparametrics, graphical models, probabilistic programming, and much more. In this course we will focus on a central theme: probabilistic inference and, to a lesser extent, modelling. Participants will leave well versed in the fundamentals of computational approaches to approximate inference in probability models.


Understand several flavors of approximate inference and trade-offs between them. Also understand a variety of canonical machine learning models.

Lecture Schedule

10:30am-12:30pm LR7

Lab Schedule

M-Fr 2pm-5pm, CDT Office 8th Flr. Thom

Homework Schedule


Calculus, linear algebra, probability and statistics, familiarity with programming

Required Books