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. Physical sciences are no exception. An article in Reviews of Moderns Physics co-authored by Lenka Zdeborová from IPhT (https://journals.aps.org/rmp/recent), covers the recent research on the interface between machine learning and physical sciences. It reviews conceptual developments in machine learning motivated by physical insights, as well as applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. It presents basic notion of machine learning methods and principles, subsequently it describes examples of how statistical physics is used to understand methods in machine learning. It then moves to describe applications of machine learning methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. It also highlights research and development into novel computing architectures aimed at accelerating machine learning.
