3.0

credits

Average Course Rating

This course is designed to cover recent results in Optimal Transport from an applied mathematical perspective. We will briefly start by covering the mathematical formulations required to understand basic applications during the first few weeks, but the majority of the class will consist of reading and presenting papers in the student's fields of interest that have significant overlap with Optimal Transport, both theoretical and applied. Faculty from JHU (and possibly other institutions) will occasionally present on how OT features in their research as well. Among other topics, we will focus on applications to Manifold Learning. The structure of this class is modeled after Equivariant Machine Learning (EN.553.743.01.FA22).