  Machine-learning driven models have proven to be powerful tools for the
identification of phases of matter. In particular, unsupervised methods hold
the promise to help discover new phases of matter without the need for any
prior theoretical knowledge. While for phases characterized by a broken
symmetry, the use of unsupervised methods has proven to be successful,
topological phases without a local order parameter seem to be much harder to
identify without supervision. Here, we use an unsupervised approach to identify
topological phases and transitions out of them. We train artificial neural nets
to relate configurational data or measurement outcomes to quantities like
temperature or tuning parameters in the Hamiltonian. The accuracy of these
predictive models can then serve as an indicator for phase transitions. We
successfully illustrate this approach on both the classical Ising gauge theory
as well as on the quantum ground state of a generalized toric code.
