  Machine learning encompasses a broad range of algorithms and modeling tools
used for a vast array of data processing tasks, which has entered most
scientific disciplines in recent years. We review in a selective way the recent
research on the interface between machine learning and physical sciences. This
includes conceptual developments in machine learning (ML) motivated by physical
insights, applications of machine learning techniques to several domains in
physics, and cross-fertilization between the two fields. After giving basic
notion of machine learning methods and principles, we describe examples of how
statistical physics is used to understand methods in ML. We then move to
describe applications of ML methods in particle physics and cosmology, quantum
many body physics, quantum computing, and chemical and material physics. We
also highlight research and development into novel computing architectures
aimed at accelerating ML. In each of the sections we describe recent successes
as well as domain-specific methodology and challenges.
