  High-energy muons from air shower events detected in IceCube are selected
using state of the art machine learning algorithms. Attributes to distinguish a
HE-muon event from the background of low-energy muon bundles are selected using
the mRMR algorithm and the events are classified by a random forest model. In a
subsequent analysis step the obtained sample is used to reconstruct the
atmospheric muon energy spectrum, using the unfolding software TRUEE. The
reconstructed spectrum covers an energy range from $10^4\,$GeV to $10^6\,$GeV.
The general analysis scheme is presented, including results using the first
year of data taken with IceCube in its complete configuration with $86$
instrumented strings.
