  The precise reconstruction of jet transverse momenta in heavy-ion collisions
is a challenging task. A major obstacle is the large number of uncorrelated
(mainly) low-$p_\mathrm{T}$ particles overlaying the jets. Strong
region-to-region fluctuations of this background complicate the jet measurement
and lead to significant uncertainties. We developed a novel approach to correct
jet momenta (or energies) for the underlying background in heavy-ion
collisions. The approach allows the measurement of jets down to extremely low
transverse momenta and for large resolution $R$ by making use of common Machine
Learning techniques to estimate the jet transverse momentum based on several
parameters. In this conference proceeding, we will present transverse momentum
spectra and nuclear modification factors of track-based jets that have been
corrected by this Machine Learning approach and comparisons to published
results where possible. The analysis was performed on Pb-Pb collisions at
$\sqrt{s_\mathrm{NN}} = 5.02$ TeV recorded with the ALICE detector and measures
jets with large resolution parameters for low momenta, unprecedented thus far
in data on heavy-ion collisions.
