  Machine learning has been applied to several problems in particle physics
research, beginning with applications to high-level physics analysis in the
1990s and 2000s, followed by an explosion of applications in particle and event
identification and reconstruction in the 2010s. In this document we discuss
promising future research and development areas for machine learning in
particle physics. We detail a roadmap for their implementation, software and
hardware resource requirements, collaborative initiatives with the data science
community, academia and industry, and training the particle physics community
in data science. The main objective of the document is to connect and motivate
these areas of research and development with the physics drivers of the
High-Luminosity Large Hadron Collider and future neutrino experiments and
identify the resource needs for their implementation. Additionally we identify
areas where collaboration with external communities will be of great benefit.
