Course Topics & Schedule
Lecture slides for the various parts of the tutorial are linked below:
- Nonparametric Modeling, Learning, and Inference
- Hierarchical Models
- Infinite Hidden Markov Models
- Infinite Hidden Markov Trees
- Spatial Modeling via Gaussian Processes
Specific topics covered during this half-day tutorial include:
- Nonparametric clustering and Chinese restaurant processes
- Nonparametric latent feature models and Indian buffet processes
- Underlying stochastic processes: Dirichlet processes, beta processes, & stick-breaking
- Markov chain Monte Carlo learning algorithms, including Gibbs samplers
- Variational learning algorithms, including mean field methods
- Infinite hidden Markov models and hidden Markov trees
- Hierarchical clustering and topic models
- Spatially dependent modeling via kernels and Gaussian processes
- Illustrative applications: clustering, image denoising, image segmentation, object modeling and recognition, temporal activity and motion modeling
More information about these topics, including references, can be found on the webpage for a Fall 2011 graduate seminar, Brown CSCI 2950-P: Applied Bayesian Nonparametrics.