Machine learning is rapidly reshaping condensed matter and quantum many-body physics, enabling new approaches to understanding strongly correlated electrons, atomic matter, quantum devices, and more. At the same time, the learning algorithms themselves present fascinating phenomena for physicists to study, from generalization, to emergence, to the striking scaling laws observed in modern large language models. This summer school will introduce graduate students to the growing exchange of ideas between machine learning and many-body physics. The program will provide foundations in modern machine learning theory and methods, and explore applications to superconductivity, magnetism, atomic systems, and quantum simulators, including both a data-driven as well as a variational simulation perspective. Lectures from physicists and machine learning researchers will highlight recent progress and the growing exchange of ideas between these fields. The goal is to equip students with practical tools for frontier problems in condensed matter physics, while also showing how ideas from statistical mechanics, quantum many-body physics, and field theory can help illuminate the learning problem itself.

The Boulder Summer School in Condensed Matter and Materials Physics has been established to provide education for advanced graduate students and postdocs working in frontiers of condensed matter physics, materials science and related fields. The goal is to enable students to work at the frontiers of science and technology by providing expert training not easily available within the traditional system of graduate education and postdoctoral apprenticeship. The school is supported by the National Science Foundation, with additional funding provided by the University of Colorado, and meets annually during July at CU Physics Department, Boulder, Colorado.