  This paper considers the problem of partially observed optimal control for
forward stochastic systems which are driven by Brownian motions and an
independent Poisson random measure with a feature that the cost functional is
of mean-field type. When all the system coefficients and the objective
performance functionals are allowed to be random, possibly non-Markovian,
Malliavin calculus is employed to derive a maximum principle for the optimal
control of such a system where the adjointprocess is explicitly expressed. We
also investigate the mean-field type optimal control problems for systems
driven by mean-field type stochastic differential equations (SDEs in short)
with jump processes, in which the coefficients contain not only the state
process but also its marginal distribution under partially observed
information. The maximum principle is established using convex variational
technique with an illustrating example about linear-quadratic optimal control.
