  Over the last years, machine learning tools have been successfully applied to
a wealth of problems in high-energy physics. A typical example is the
classification of physics objects. Supervised machine learning methods allow
for significant improvements in classification problems by taking into account
observable correlations and by learning the optimal selection from examples,
e.g. from Monte Carlo simulations. Even more promising is the usage of deep
learning techniques. Methods like deep convolutional networks might be able to
catch features from low-level parameters that are not exploited by default
cut-based methods.
  These ideas could be particularly beneficial for measurements in heavy-ion
collisions, because of the very large multiplicities. Indeed, machine learning
methods potentially perform much better in systems with a large number of
degrees of freedom compared to cut-based methods. Moreover, many key heavy-ion
observables are most interesting at low transverse momentum where the
underlying event is dominant and the signal-to-noise ratio is quite low.
  In this work, recent developments of machine- and deep learning applications
in heavy-ion collisions with ALICE will be presented, with focus on a deep
learning-based b-jet tagging approach and the measurement of low-mass
dielectrons. While the b-jet tagger is based on a mixture of shallow
fully-connected and deep convolutional networks, the low-mass dielectron
measurement uses gradient boosting and shallow neural networks. Both methods
are very promising compared to default cut-based methods.
