In this talk, I will discuss the connections between physical nonequilibrium systems and common algorithms employed in machine learning. I will report how machine learning has been used to expand the scope of physical nonequilibrium systems that can be effectively studied computationally. The interpretation of the optimization procedure as a nonequilibrium dynamics will be also examined. Specific examples in reinforcement learning will be highlighted.