  Machine learning (ML) is a subfield of artificial intelligence. The term
applies broadly to a collection of computational algorithms and techniques that
train systems from raw data rather than a priori models. ML techniques are now
technologically mature enough to be applied to particle accelerators, and we
expect that ML will become an increasingly valuable tool to meet new demands
for beam energy, brightness, and stability. The intent of this white paper is
to provide a high-level introduction to problems in accelerator science and
operation where incorporating ML-based approaches may provide significant
benefit. We review ML techniques currently being investigated at particle
accelerator facilities, and we place specific emphasis on active research
efforts and promising exploratory results. We also identify new applications
and discuss their feasibility, along with the required data and infrastructure
strategies. We conclude with a set of guidelines and recommendations for
laboratory managers and administrators, emphasizing the logistical and
technological requirements for successfully adopting this technology. This
white paper also serves as a summary of the discussion from a recent workshop
held at SLAC on ML for particle accelerators.
