APMTH 220: Geometric Methods for Machine Learning

Recently, there has been a surge of interest in exploiting geometric structure in data and models in Machine Learning. This course will give an overview of this emerging research area and its mathematical foundation, with a focus on recent literature and open problems. We will cover a range of topics at the intersection of Geometry and Machine Learning including Basic Differential Geometry, Graph Representation Learning, Manifold Learning, Graph Neural Networks, Machine Learning on Manifolds, and Geometric Deep Learning. Lectures will be complemented by student-led discussions of relevant papers.

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