  The construction of good effective models is an essential part of
understanding and simulating complex systems in many areas of science. It is a
particular challenge for correlated many body quantum systems displaying
emergent physics. We propose a machine learning approach that optimizes an
effective model based on an estimation of its partition function. The success
of the method is demonstrated by application to the single impurity Anderson
model and double quantum dots, where non-perturbative results are obtained for
the old problem of mapping to effective Kondo models. For quantum impurity
parent Hamiltonians, we derive an alternative approach based on learning from
observables. When mapping to minimal effective models, emergent scales may not
be captured by observable learning, while partition function learning may not
reproduce all observables.
