  The many ways in which machine and deep learning are transforming the
analysis and simulation of data in particle physics are reviewed. The main
methods based on boosted decision trees and various types of neural networks
are introduced, and cutting-edge applications in the experimental and
theoretical/phenomenological domains are highlighted. After describing the
challenges in the application of these novel analysis techniques, the review
concludes by discussing the interactions between physics and machine learning
as a two-way street enriching both disciplines and helping to meet the present
and future challenges of data-intensive science at the energy and intensity
frontiers.

Data Analysis, Statistics and Probability;High Energy Physics - Experiment;High Energy Physics - Phenomenology;Computational Physics