About the Course

This course is an introduction to the emerging range of problems at the interface of calculus of variations and partial differential equations on the one hand and theoretical data science on the other hand. We will develop general tools in the calculus of variations, optimal transportation and gradient flows, and apply them to problems in machine learning, such as clustering and semi-supervised learning and approximability questions.

Prerequisites

Working knowledge in Functional Analysis and PDEs are assumed. No knowledge in data science / machine learning is necessary to follow the course.

Lectures

Tuesdays 10am-noon
Fridays 10am-noon

Office Hours

You can find me in my office (Villa Maria, Room 1.002) by appointment or during my regular office hour on

Tuesdays noon-1pm