Applications of machine learning to data from number theory

Abstract: In this talk, we apply machine learning techniques to various data from the L-functions and modular forms database (LMFDB) and show that a machine can be trained to distinguish objects in number theory according to their standard invariants. The applications in this talk will include class numbers of quadratic number fields, ranks of elliptic curves, Sato-Tate groups of genus 2 curves. This is joint work with Yang-Hui He and Thomas Oliver.

machine learningalgebraic geometrynumber theory

Audience: researchers in the topic

DANGER: Data, Numbers, and Geometry