  We study machine learning of phenomenologically relevant properties of string
compactifications, which arise in the context of heterotic line bundle models.
Both supervised and unsupervised learning are considered. We find that, for a
fixed compactification manifold, relatively small neural networks are capable
of distinguishing consistent line bundle models with the correct gauge group
and the correct chiral asymmetry from random models without these properties.
The same distinction can also be achieved in the context of unsupervised
learning, using an auto-encoder. Learning non-topological properties,
specifically the number of Higgs multiplets, turns out to be more difficult,
but is possible using sizeable networks and feature-enhanced data sets.
