Machine learning for the analysis of hard probes

Modern high-energy experimental facilities such as the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC) are collecting more data and making more complex measurements of hard and electromagnetic probes than ever before. Increasingly, machine learning has proven to be a valuable tool for these efforts that can be used throughout the analysis pipeline from data collection to analysis. In the coming decades, such techniques will become necessary at future facilities such as the Electron Ion Collider (EIC) and the High Luminosity LHC (HL-LHC). These proceedings summarize a selection of recent developments on the use of machine learning as an analysis techniques for the physics of hard and electromagnetic probes and provide an outlook for its future use.