Lecture: Thursday 9am - 11am weekly
Exercise: Thursday 11am-1pm bi-weekly
Scope: Optimal Transport (OT) is an exciting field at the intersection of Probability Theory, Geometry, and Partial Differential Equations (PDEs). It provides a framework to compare probability distributions by finding a path between them that minimizes a certain cost or energy functional. This interdisciplinary subject has found numerous applications in the theory of PDEs, probability theory, and machine learning. In this course, we will delve into the fundamental theory of Optimal Transport, explore applications, and discuss numerical methods to solve OT problems effectively. The course is aimed at students with diverse backgrounds who intend to specialize in Applied Analysis, Probability Theory, Optimization, or Machine Learning.
Prerequisites: Functional analysis
Hours per term: 2+1 SWS
Literature