  The Kronecker coefficients are the decomposition multiplicities of the tensor
product of two irreducible representations of the symmetric group. Unlike the
Littlewood--Richardson coefficients, which are the analogues for the general
linear group, there is no known combinatorial description of the Kronecker
coefficients, and it is an NP-hard problem to decide whether a given Kronecker
coefficient is zero or not. In this paper, we show that standard
machine-learning algorithms such as Nearest Neighbors, Convolutional Neural
Networks and Gradient Boosting Decision Trees may be trained to predict whether
a given Kronecker coefficient is zero or not. Our results show that a trained
machine can efficiently perform this binary classification with high accuracy
($\approx 0.98$).
