  The advent of large-scale stellar spectroscopic surveys naturally leads to
the implementation of machine learning techniques to isolate, for example,
small sub-samples of potentially interesting stars from the full data set. A
recent example is the application of the t-SNE statistical method to
$\sim$600,000 stellar spectra from the GALAH survey in order to identify a
sample of candidate extremely metal-poor (EMP, [Fe/H] $\leq$ -3) stars. We
report the outcome of low-resolution spectroscopic follow-up of 83 GALAH EMP
candidates that lack any previous metallicity estimates. Overall, the
statistical selection is found to be efficient ($\sim$one-third of the
candidates have [Fe/H] $\leq$ -2.75) with low contamination ($<$10% have [Fe/H]
$>$ -2), and with a metallicity distribution function that is consistent with
previous work. Five stars are found to have [Fe/H] $\leq$ -3.0, one of which is
a main sequence turnoff star. Two other stars are revealed as likely
carbon-enhanced metal-poor (CEMP) stars of type CEMP-$s$, and a known carbon
star is re-identified. The results indicate that the statistical selection
approach employed was successful, and therefore it can be applied to
forthcoming even larger stellar spectroscopic surveys with the expectation of
similar positive outcomes.
