  We demonstrate, by means of a convolutional neural network, that the features
learned in the two-dimensional Ising model are sufficiently universal to
predict the structure of symmetry-breaking phase transitions in considered
systems irrespective of the universality class, order, and the presence of
discrete or continuous degrees of freedom. No prior knowledge about the
existence of a phase transition is required in the target system and its entire
parameter space can be scanned with multiple histogram reweighting to discover
one. We establish our approach in q-state Potts models and perform a
calculation for the critical coupling and the critical exponents of the
$\phi^{4}$ scalar field theory using quantities derived from the neural network
implementation. We view the machine learning algorithm as a mapping that
associates each configuration across different systems to its corresponding
phase and elaborate on implications for the discovery of unknown phase
transitions.
