  In this paper we study the use of Machine Learning techniques to exploit
kinematic information in VH, the production of a Higgs in association with a
massive vector boson. We parametrise the effect of new physics in terms of the
SMEFT framework. We find that the use of a shallow neural network allows us to
dramatically increase the sensitivity to deviations in VH respect to previous
estimates. We also discuss the relation between the usual measures of
performance in Machine Learning, such as AUC or accuracy, with the more adept
measure of Asimov significance. This relation is particularly relevant when
parametrising systematic uncertainties. Our results show the potential of
incorporating Machine Learning techniques to the SMEFT studies using the
current datasets.
