Mathematics and machine learning program

over 80 participants from many subfields of mathematics, physics and computer science.For eight weeks we explored the new opportunities created by applying the most recent developments in machine learning to mathematical problems old and new, proposed problems and formed working groups, and began in-depth studies.Our work continued after the program, and many of the results are reported in the articles here.Let us briefly outline the contents by subtopic.First come papers on methods and new software developed specifically for mathematical applications.A noteworthy feature of the program was the close collaboration between mathematical and machine learning experts, and much was learned on both sides.These papers include "Int2int -a Transformer Model for Integer Sequences" by Charton on a new transformer model and "Generative Modeling for Mathematical Discovery" by Sutherland et al. on a new implementation of the funsearch method, "Merging Hazy Sets with m-Schemes: A Geometric Approach to Data Visualization" by Barth et al., "Kolmogorov-Arnold stability" by Dzhenzher and Freedman, and "Mathematical Data Science" by Douglas and Lee, which surveyed this broad area with case studies such as the discovery of murmurations.Going the other direction, there were many talks and discussions on studying machine learning using ideas and methods from mathematics and physics.This topic is represented in the issue by "Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models" by Atanasov et al., and by work to appear in later issues of ATMP.Two program weeks focused on number theory, leading to many papers: "Learning Euler factors of elliptic curves" by Babei et al., "Machine Learning Approaches to the Shafarevich-Tate Group of Elliptic Curves" by Banwait