  The Higgs boson couplings to bottom and top quarks have been measured and
agree well with the Standard Model predictions. Decays to lighter quarks and
gluons, however, remain elusive. Observing these decays is essential to
complete the picture of the Higgs boson interactions. In this work, we present
the perspectives for the 14 TeV LHC to observe the Higgs boson decay to gluon
jets assembling convolutional neural networks, trained to recognize abstract
jet images constructed embodying particle flow information, and boosted
decision trees with kinetic information from Higgs-strahlung $ZH\to
\ell^+\ell^- + gg$ events. We show that this approach might be able to observe
Higgs to gluon decays with a significance of around $2.4\sigma$ improving
significantly previous prospects based on cut-and-count analysis. An upper
bound of $BR(H\to gg)\leq 1.74\times BR^{SM}(H\to gg)$ at 95\% confidence level
after 3000 fb$^{-1}$ of data is obtained using these machine learning
techniques.
