Current course offerings

Please Note: Course profiles marked as not available may still be in development.

Course description

The course focuses on the mathematical understanding of machine learning and data science. Topics to be covered in the first half include theoretical frameworks for supervised learning, unsupervised learning, and Bayesian analysis, Akaike and Bayesian Information criteria, tradeoffs in statistical learning, Monte Carlo methods, and principal component analysis. The second half will include the theory of regularization and kernel methods, Gaussian process regression and support vector machines.

Archived offerings