First, if you are starting out, you need to see How to do good research, get it published in SIGKDD and get it cited! This is an amazing tutorial from Eamonn Keogh back in 2009, but nothing changes, right? Lots of gems in there.
Here are some tutorials from others I highly recommend, from the fabulous Video Lectures website. The titles are pretty good descriptors of content. Ideally, this is what students need to know for research in topic models and related material like Bayesian probability, graphical models, MCMC, etc.
- Ralf Herbrich of Amazon, Distributed, Real-Time Bayesian Learning in Online Service, given at ACML 2013: this is the first hour of a 3 hour tutorial, its really an introduction to probabilities, graphical models and inference.
- Padhraic Smyth, Analyzing Text and Social Network Data with Probabilistic Models, 2012; actually an invited talk but the first half is a great tutorial on using probabilities and simple text models leading up to topic models
- Zoubin Ghahramani, Graphical Models, 2012
- Neil Lawrence, Learning with Probabilities, 2010
- Yee Whye Teh, Bayesian Nonparametrics, 2011
- Mark Johnson, Probabilistic Models for Computational Linguistics, 2010
- Sam Roweis, Machine Learning, Probability and Graphical Models, 2007, a bit more technical than the others, but a good review of what I like students to know
- David Blei, Topic Models, 2009, a tour of different kinds of models and the ideas behind them
- (just slides, no video) Percy Liang and Dan Klein, Structured Bayesian Nonparametric Models with Variational Inference, ACL tutorial 2007, a heavy duty, in-depth coverage.
- Michael Jordan, Dirichlet Processes, Chinese Restaurant Processes, and all that, 2005, an amazing invited talk that covers a lot of material mentioned above; this is also what I like students to know; not a tutorial but a lot of the material presented in a tutorial way
Also, if you’re into graphical models, the best set of lectures on theoretical material I know is from Prof. Stephen Lauritzen (now retired), Graphical Models and Inference, a course presented at Oxfords Statistics some time ago. He is the most outstanding researcher in this field.
Bayesian Models 