  With the advent of dedicated photometric space missions, the ability to
rapidly process huge catalogues of stars has become paramount. Bellinger and
Angelou et al. (2016) recently introduced a new method based on machine
learning for inferring the stellar parameters of main-sequence stars exhibiting
solar-like oscillations. The method makes precise predictions that are
consistent with other methods, but with the advantages of being able to explore
many more parameters while costing practically no time. Here we apply the
method to 52 so-called "LEGACY" main-sequence stars observed by the Kepler
space mission. For each star, we present estimates and uncertainties of mass,
age, radius, luminosity, core hydrogen abundance, surface helium abundance,
surface gravity, initial helium abundance, and initial metallicity as well as
estimates of their evolutionary model parameters of mixing length, overshooting
coefficient, and diffusion multiplication factor. We obtain median
uncertainties in stellar age, mass, and radius of 14.8%, 3.6%, and 1.7%,
respectively.
  The source code for all analyses and for all figures appearing in this
manuscript can be found electronically at:
https://github.com/earlbellinger/asteroseismology
