Furthermore, the classification is a best attempt and may have flaws and Machine learning (ML) is a generic term used community input is requested if (a) we have missed to describe any automated inference procedure, a paper you think should be included, (b) a paper broadly defined. As such, machine learning plays a has been misclassified, or (c) a citation for a paper key role in nearly all areas of high energy physics is not correct or if the journal information is now (HEP). Traditionally, machine learning has been available. The review is built automatically from synonymous with "multivariate techniques", with the contents of the Git repository on GitHub using Boosted Decision Trees as the community favorite L

Machine learning is an important research area in particle physics, 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 in machine learning in particle physics with 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.