  Machine learning methods are applied to finding the Green's function of the
Anderson impurity model, a basic model system of quantum many-body
condensed-matter physics. Different methods of parametrizing the Green's
function are investigated; a representation in terms of Legendre polynomials is
found to be superior due to its limited number of coefficients and its
applicability to state of the art methods of solution. The dependence of the
errors on the size of the training set is determined. The results indicate that
a machine learning approach to dynamical mean-field theory may be feasible.
