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

Development of a Machine Learning Based Analysis Chain for the Measurement of Atmospheric Muon Spectra with IceCube