  Machine learning is a rapidly growing field with the potential to
revolutionize many areas of science, including physics. This review provides a
brief overview of machine learning in physics, covering the main concepts of
supervised, unsupervised, and reinforcement learning, as well as more
specialized topics such as causal inference, symbolic regression, and deep
learning. We present some of the principal applications of machine learning in
physics and discuss the associated challenges and perspectives.
