EECS E6892 Topics in Information Processing
Bayesian Models for Machine Learning
Columbia University, Fall 2015
Instructor: John Paisley
Location: 750 Schapiro CEPSR
Time: Thursday 4:10-6:40pm
Instructor office hours: Monday 11am-1pm, Seeley Mudd 422
TA office hours: Dawen Liang, email@example.com, hrs: Tuesday 2-4pm @ Shapiro CEPSR 7LE4 (7th floor)
Aonan Zhang, firstname.lastname@example.org, hrs: Wednesday 7-9pm @ CS TA-Room in Mudd (1st floor)
Synopsis: This intermediate-level machine learning course will focus on Bayesian approaches to machine learning. Topics will include mixed-membership models, latent factor models and Bayesian nonparametric methods. We will also focus on mean-field variational Bayesian inference, an optimization-based 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, 15-15-20-20% split), midterm exam (30%)