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-$z$.
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