  Machine-learning (ML) techniques are explored to identify and classify
hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks.
Techniques without ML have also been evaluated and are included for comparison.
The identification performances of a variety of algorithms are characterized in
simulated events and directly compared with data. The algorithms are validated
using proton-proton collision data at $\sqrt{s} =$ 13 TeV, corresponding to an
integrated luminosity of 35.9 fb$^{-1}$. Systematic uncertainties are assessed
by comparing the results obtained using simulation and collision data. The new
techniques studied in this paper provide significant performance improvements
over non-ML techniques, reducing the background rate by up to an order of
magnitude at the same signal efficiency.
