EECS E6892 Topics in Information Processing
Bayesian Models for Machine Learning Columbia University, Fall 2015 Instructor: John Paisley Location: 750 Schapiro CEPSR Time: Thursday 4:106:40pm Instructor office hours: Monday 11am1pm, Seeley Mudd 422 TA office hours: Dawen Liang, dliang@ee.columbia.edu, hrs: Tuesday 24pm @ Shapiro CEPSR 7LE4 (7th floor) Aonan Zhang, az2385@columbia.edu, hrs: Wednesday 79pm @ CS TARoom in Mudd (1st floor) Synopsis: This intermediatelevel machine learning course will focus on Bayesian approaches to machine learning. Topics will include mixedmembership models, latent factor models and Bayesian nonparametric methods. We will also focus on meanfield variational Bayesian inference, an optimizationbased approach to approximate posterior learning. Applications of these methods include image processing, topic modeling, collaborative filtering and recommendation systems. We will discuss a selection of these in class. Text: There is no required text. Much of the material can be found in Pattern Recognition and Machine Learning by Christopher Bishop. Other suggested papers will be posted here. Grading: 4 homeworks (70% total, 15152020% split), midterm exam (30%)

