The numerical resolution of high-dimensional partial differential equations (PDEs) and stochastic control is a challenging problem in applied mathematics. Over the last five years, several deep neural networks-based algorithms have been proposed and have show their great efficiency for tackling these issues. In this talk, we give an introduction to this field of research, review the main results in this literature, and present some new developments, notably regarding mean-field control problems and Master equation in Wasserstein space. Speaker(s): Huyen Pham (Paris 7)