  The recent advances in machine learning algorithms have boosted the
application of these techniques to the field of condensed matter physics, in
order e.g. to classify the phases of matter at equilibrium or to predict the
real-time dynamics of a large class of physical models. Typically in these
works, a machine learning algorithm is trained and tested on data coming from
the same physical model. Here we demonstrate that unsupervised and supervised
machine learning techniques are able to predict phases of a non-exactly
solvable model when trained on data of a solvable model. In particular, we
employ a training set made by single-particle correlation functions of a
non-interacting quantum wire and by using principal component analysis, k-means
clustering, and convolutional neural networks we reconstruct the phase diagram
of an interacting superconductor. We show that both the principal component
analysis and the convolutional neural networks trained on the data of the
non-interacting model can identify the topological phases of the interacting
model with a high degree of accuracy. Our findings indicate that non-trivial
phases of matter emerging from the presence of interactions can be identified
by means of unsupervised and supervised techniques applied to data of
non-interacting systems.
