  We introduce a deep machine learning approach to studying quasar microlensing
light curves for the first time by analyzing hundreds of thousands of simulated
light curves with respect to the accretion disc size and temperature profile.
Our results indicate that it is possible to successfully classify very large
numbers of diverse light curve data and measure the accretion disc structure.
The detailed shape of the accretion disc brightness profile is found to play a
negligible role, in agreement with Mortonson et al. (2005). The speed and
efficiency of our deep machine learning approach is ideal for quantifying
physical properties in a `big-data' problem setup. This proposed approach looks
promising for analyzing decade-long light curves for thousands of microlensed
quasars, expected to be provided by the Large Synoptic Survey Telescope.
