  We study the problem of determining the Hamiltonian of a fully connected
Ising Spin Glass of $N$ units from a set of measurements, whose sizes needs to
be ${\cal O}(N^2)$ bits. The student-teacher scenario, used to study learning
in feed-forward neural networks, is here extended to spin systems with
arbitrary couplings. The set of measurements consists of data about the local
minima of the rugged energy landscape. We compare simulations and analytical
approximations for the resulting learning curves obtained by using different
algorithms.
