Machine Learning and the Differential Geometry of the Data

In this talk I will discuss some mathematical aspects of machine learning. I will start by describing the basic problems and challenges of learning from high-dimensional data and proceed by concentrating on the role of understanding "the shape of the data" through its differential geometry as given by the Laplace-Beltrami operator and the corresponding heat kernel. I will describe various connections, algorithms and theoretical results.