Teaching: Courses & Tutorials

In Fall 2017, I am teaching CS177: Applications of Probability in Computer Science.


Applied Bayesian Nonparametrics

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.


Probabilistic Graphical Models

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.


Machine Learning

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.


Probability in Computer Science

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.