  Over the past five years, modern machine learning has been quietly
revolutionizing particle physics. Old methodology is being outdated and
entirely new ways of thinking about data are becoming commonplace. This article
will review some aspects of the natural synergy between modern machine learning
and particle physics, focusing on applications at the Large Hadron Collider. A
sampling of examples is given, from signal/background discrimination tasks
using supervised learning to direct data-driven approaches. Some comments on
persistent challenges and possible future directions for the field are included
at the end.
