PIMS is pleased to offer two network-wide graduate courses in fall 2021

  • Date: 07/26/2021

Members of the PIMS community are encouraged to begin the registration process for Fall 2021 network wide courses. Details on the course offering are mentioned below.

 

Charles Doran (University of Alberta) will teach a course on Differential Equations in Algebraic Geometry, addressing the question "What can differential equations tell us about the solutions to systems of algebraic equations?" or, conversely, what are the special properties of differential equations, and their solutions, that “come from geometry”? This course will give a concrete introduction to transcendental algebraic geometry using tools from both algebra and analysis. The course will include special guest lectures by the following experts: Adrian Clingher (University of Missouri at St. Louis), Shinobu Hosono (Gakushuin University), Matt Kerr (Washington University in St. Louis), Andreas Malmendier (Utah State University/ University of Connecticut), Hossein Movasati (Instituto de Matematica Pura e Aplicada), and Pierre Vanhove (Institut de Physique Théorique, CEA-Saclay). This course is currently accepting applications through the Western Deans Agreement. 

 

Soumik Pal, Zaid Harchaoui (University of Washington) and Young-Heon Kim (University of British Columbia) will teach a course on Optimal Transport and Machine Learning. A number of problems equivalent or related to the Monge-Kantorovich Optimal Transport (OT) problem have appeared in recent years in machine learning, and in data science at large. The fruitful connections between the two fields have led to several important advances impacting both. The first part of the course will cover the mathematical basics of OT and introduce the geometry of Wasserstein spaces. The second part of the course will cover computational aspects of OT and describe the central role played by OT in convergence analysis of stochastic algorithms for deep learning, in distributionally robust statistical learning, and in combinatorial or geometrical problems arising in data science applications.

 

For more details see https://courses.pims.math.ca.