  This review deals with Restricted Boltzmann Machine (RBM) under the light of
statistical physics. The RBM is a classical family of Machine learning (ML)
models which played a central role in the development of deep learning. Viewing
it as a Spin Glass model and exhibiting various links with other models of
statistical physics, we gather recent results dealing with mean-field theory in
this context. First the functioning of the RBM can be analyzed via the phase
diagrams obtained for various statistical ensembles of RBM leading in
particular to identify a {\it compositional phase} where a small number of
features or modes are combined to form complex patterns. Then we discuss recent
works either able to devise mean-field based learning algorithms; either able
to reproduce generic aspects of the learning process from some {\it ensemble
dynamics equations} or/and from linear stability arguments.
