In Spring 2018, I am teaching CS274B: Learning in Graphical Models.

Bayesian nonparametric (BNP) models define distributions on infinite-dimensional spaces of functions, partitions, or other combinatorial structures. They lead to flexible, data-driven unsupervised learning algorithms, and models whose internal structure continually grows and adapts to new observations.

- Tutorial at CVPR 2012: Hierarchical BNP models, learning & inference algorithms, temporal & spatial dependencies, applications to image & video analysis.
- Brown CS295P (Fall 2011): Graduate course on BNP models, inference, & applications.
- Erik Sudderth's PhD thesis (Chap. 2) has a tutorial on hierarchical Dirichlet processes.
- Peter Orbanz has collected a list of other tutorials on Bayesian nonparametrics.

Graphical models enable scalable probabilistic modeling by decomposing complex distributions into local interactions. This graduate course explores state-of-the-art variational and Monte Carlo methods for statistical learning with probabilistic graphical models.

- UCI CS274B: Learning in Graphical Models is offered in Spring 2018.
- Brown CS242: Probabilistic Graphical Models was taught in Fall 2016, Fall 2014, & Spring 2013.
- Brown CS295P (Spring 2010) was an earlier seminar-style course on graphical models.
- Erik Sudderth's PhD thesis (Chap. 2) reviews graphical models & exponential families.
- Erik Sudderth & Bill Freeman wrote a tutorial on signal & image processing with belief propagation.
- Michael Jordan wrote an introduction to graphical models.

How can artificial systems learn from examples, and discover information buried in massive datasets? This advanced undergraduate course explores the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised data analysis.

- UCI CS178: Machine Learning & Data Mining was taught in Winter 2018.
- Brown CS142: Machine Learning was taught in Fall 2015, Fall 2013, Spring 2012, Spring 2011, Fall 2009.
- Machine learning textbooks: Bishop, Murphy, Barber, Hastie & Tibshirani & Friedman

Probabilistic methods and statistical reasoning play major roles in machine learning, security, web search, robotics, program verification, and more. This introductory course on probability and statistics emphasizes computational methods and computer science applications.

- UCI CS177: Applications of Probability in Computer Science was taught in Fall 2017.
- Brown CS145: Probability & Computing was taught in Spring 2016 & Spring 2015.
- Probability textbooks: Bertsekas & Tsitsiklis, Pitman