  The latest techniques from Neural Networks and Support Vector Machines (SVM)
are used to investigate geometric properties of Complete Intersection
Calabi-Yau (CICY) threefolds, a class of manifolds that facilitate string model
building. An advanced neural network classifier and SVM are employed to (1)
learn Hodge numbers and report a remarkable improvement over previous efforts,
(2) query for favourability, and (3) predict discrete symmetries, a highly
imbalanced problem to which both Synthetic Minority Oversampling Technique
(SMOTE) and permutations of the CICY matrix are used to decrease the class
imbalance and improve performance. In each case study, we employ a genetic
algorithm to optimise the hyperparameters of the neural network. We demonstrate
that our approach provides quick diagnostic tools capable of shortlisting
quasi-realistic string models based on compactification over smooth CICYs and
further supports the paradigm that classes of problems in algebraic geometry
can be machine learned.
