  We initiate the study of applications of machine learning to Seiberg duality,
focusing on the case of quiver gauge theories, a problem also of interest in
mathematics in the context of cluster algebras. Within the general theme of
Seiberg duality, we define and explore a variety of interesting questions,
broadly divided into the binary determination of whether a pair of theories
picked from a series of duality classes are dual to each other, as well as the
multi-class determination of the duality class to which a given theory belongs.
We study how the performance of machine learning depends on several variables,
including number of classes and mutation type (finite or infinite). In
addition, we evaluate the relative advantages of Naive Bayes classifiers versus
Convolutional Neural Networks. Finally, we also investigate how the results are
affected by the inclusion of additional data, such as ranks of gauge/flavor
groups and certain variables motivated by the existence of underlying
Diophantine equations. In all questions considered, high accuracy and
confidence can be achieved.
