  Machine learning for partial differential equations (PDEs) is a hot topic. In
this paper we introduce and analyse a Deep BSDE scheme for nonlinear
integro-PDEs with unbounded nonlocal operators -problems arising in e.g.
stochastic control and games involving infinite activity jump-processes. The
scheme is based on a stochastic forward-backward SDE representation of the
solution of the PDE and (i) approximation of small jumps by a Gaussian process,
(ii) simulation of the forward part, and (iii) a neural net regression for the
backward part. Unlike grid-based schemes, it does not suffer from the curse of
dimensionality and is therefore suitable for high dimensional problems. The
scheme is designed to be convergent even in the infinite activity/unbounded
nonlocal operator case. A full convergence analysis is given and constitutes
the main part of the paper.
