  Owing to the remarkable photometric precision of space observatories like
Kepler, stellar and planetary systems beyond our own are now being
characterized en masse for the first time. These characterizations are pivotal
for endeavors such as searching for Earth-like planets and solar twins,
understanding the mechanisms that govern stellar evolution, and tracing the
dynamics of our Galaxy. The volume of data that is becoming available, however,
brings with it the need to process this information accurately and rapidly.
While existing methods can constrain fundamental stellar parameters such as
ages, masses, and radii from these observations, they require substantial
computational efforts to do so.
  We develop a method based on machine learning for rapidly estimating
fundamental parameters of main-sequence solar-like stars from classical and
asteroseismic observations. We first demonstrate this method on a
hare-and-hound exercise and then apply it to the Sun, 16 Cyg A & B, and 34
planet-hosting candidates that have been observed by the Kepler spacecraft. We
find that our estimates and their associated uncertainties are comparable to
the results of other methods, but with the additional benefit of being able to
explore many more stellar parameters while using much less computation time. We
furthermore use this method to present evidence for an empirical diffusion-mass
relation. Our method is open source and freely available for the community to
use.
  The source code for all analyses and for all figures appearing in this
manuscript can be found electronically at
https://github.com/earlbellinger/asteroseismology
