Nonparametric Bayesian methods are an increasingly important tool in applied machine learning and statistics. We will focus on the Dirichlet process (DP) and its cousins. Topics include the original development of the DP, the suite of approximate inference techniques that have made it practical for data analysis, and recently developed variants.
Readings are posted on the syllabus.
Monday 1:30PM-4:20PM, location CS 402
David M. Blei
204 CS Building
blei [at] cs.princeton.edu
Office hours: TBA
All students are expected to be comfortable with probability and basic statistics. Please see Prof. Blei if unsure whether you meet the requirements.
The course consists of readings, lectures, and discussions. The requirements for everyone (including auditors) are to do the reading, participate in class, and scribe for at least one class. For those taking the course for a grade, the requirements include a final project or report.
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