  We present a measurement of the two-point autocorrelation function of
photometrically-selected, high-$z$ quasars over $\sim$ 100 deg$^2$ on the Sloan
Digitial Sky Survey Stripe 82 field. Selection is performed using three
machine-learning algorithms, trained on known high-$z$ quasar colors, in a
six-dimensional, optical/mid-infrared color space. Optical data from the Sloan
Digitial Sky Survey is combined with overlapping deep mid-infrared data from
the \emph{Spitzer} IRAC Equatorial Survey and the \emph{Spitzer}-HETDEX
Exploratory Large-area survey. The selected quasar sample consists of 1378
objects and contains both spectroscopically-confirmed quasars and
photometrically-selected quasar candidates. These objects span a redshift range
of $2.9 \leq z \leq 5.1$ and are generally fainter than $i=20.2$; a regime
which has lacked sufficient number density to perform autocorrelation function
measurements of photometrically-classified quasars. We compute the angular
correlation function of these data, marginally detecting quasar clustering. We
fit a single power-law with an index of $\delta = 1.39 \pm 0.618$ and amplitude
of $\theta_0 = 0.71 \pm 0.546$ arcmin. A dark-matter model is fit to the
angular correlation function to estimate the linear bias. At the average
redshift of our survey ($\langle z \rangle = 3.38$) the bias is $b = 6.78 \pm
1.79$. Using this bias, we calculate a characteristic dark-matter halo mass of
1.70--9.83$\times 10^{12}h^{-1} M_{\odot}$. Our bias estimate suggests that
quasar feedback intermittently shuts down the accretion of gas onto the central
super-massive black hole at early times. If confirmed, these results hint at a
level of luminosity dependence in the clustering of quasars at high-$z$.
