  Thermodynamics could be seen as an expression of physics at a high epistemic
level. As such, its potential as an inductive bias to help machine learning
procedures attain accurate and credible predictions has been recently realized
in many fields. We review how thermodynamics provides helpful insights in the
learning process. At the same time, we study the influence of aspects such as
the scale at which a given phenomenon is to be described, the choice of
relevant variables for this description or the different techniques available
for the learning process.
