Machine Learning II (2007/2008)

4/18 4/25 Nonparametric Bayes

We introduce the nonparametric Bayesian models popular in machine learning in this two lectures.

In the first lecture we start with an introduction to the Dirichlet process. We start with some intuition concerning the need for nonparametric models, and the idea of partitions of data. From there the various perspectives on the Dirichlet process are presented, specifically the Chinese restaurant process and the stick-breaking construction. We then cover the hierarchical Dirichlet process, the nested Dirichlet process, and the Indian Buffet process.


Reading material:

In the second week students will present the following papers: