See below for an archive of my previous teaching at Brown University.
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.
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.
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.
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.