  The stars within our Galactic halo presents a snapshot of its ongoing growth
and evolution, probing galaxy formation directly. Here, we present our first
analysis of the stellar halo from detailed maps of Blue Horizontal Branch (BHB)
stars drawn from the SkyMapper Southern Sky Survey. To isolate candidate BHB
stars from the overall population, we develop a machine-learning approach
through the application of an Artificial Neural Network (ANN), resulting in a
relatively pure sample of target stars. From this, we derive the absolute $u$
magnitude for the BHB sample to be $\sim2\ mag$, varying slightly with
$(v-g)_0$ and $(u-v)_0$ colours. We examine the BHB number density distribution
from 5272 candidate stars, deriving a double power-law with a break radius of
$r_s = 11.8\pm0.3\ kpc$, and inner and outer slopes of $\alpha_{in} =
-2.5\pm0.1$ and $\alpha_{out} = -4.5\pm0.3$ respectively. Through isochrone
fitting of simulated BHB stars, we find a colour-age/metallicity correlation,
with older/more metal-poor stars being bluer, and establish a parameter to
indicate this age (or metallicity) variation. Using this, we construct the
three-dimensional population distribution of BHB stars in the halo and identify
significant substructure. Finally, in agreement with previous studies, we also
identify a systemic age/metallicity shift spanning $\sim3\ kpc$ to $ \sim20\
kpc$ in galactocentric distance.
