Below you can find information, videos, and slides for some of the tutorials and courses I have given.
An introduction to Bayesian cluster models with an unbounded number of potential clusters---as well as a generalization of clustering in which each data point can belong to multiple latent groups. The tutorial places particular emphasis on the following popular nonparametric Bayesian constructions: Chinese restaurant processes, Indian buffet processes, Dirichlet processes, and beta processes. The talk assumes some existing knowledge of Bayes theorem and Bayesian statistics as well as passing knowledge of Markov Chain Monte Carlo algorithms.
This talk introduces clustering as a subfield of machine learning with an emphasis on practical usage. We cover the K means algorithm, cluster evaluation, different meanings of clustering, and data pre-processing. A final example illustrates how the ideas of the lecture come together when tackling real data.