  Among the first observations released to the public from the James Webb Space
Telescope (JWST) was a section of the star-forming region NGC 3324 known
colloquially as the "Cosmic Cliffs." We build a photometric catalog of the
region and test the ability of using the Probabilistic Random Forest machine
learning method to identify its Young Stellar Objects (YSOs). We find 450
candidate YSOs (cYSOs) out of 19~497 total objects within the field, 413 of
which are cYSOs not found in previous works. These classifications are verified
with several different metrics, including recall and precision. Using the
obtained probabilities of objects being YSOs, we employ a Monte Carlo approach
to determine the surface density of cYSOs in the Cosmic Cliffs, which we find
to be largely coincident with column densities derived from Herschel data, up
to a column density of 1.37 $\times$ 10$^{22}$ cm$^{-2}$. The newly determined
number and spatial distribution of YSOs in the Cosmic Cliffs demonstrate that
JWST is far more capable of detecting YSOs in dusty regions than Spitzer.
Comparisons of the observed colors and brightness of faint cYSOs with those of
pre-main-sequence models suggest JWST has detected a significant population of
sub-stellar YSOs in the Cosmic Cliffs. The size of this population further
suggests previous estimates of star formation efficiencies in molecular clouds
have been systematically low.
