  Machine learning entails a broad range of techniques that have been widely
used in Science and Engineering since decades. High-energy physics has also
profited from the power of these tools for advanced analysis of colliders data.
It is only up until recently that Machine Learning has started to be applied
successfully in the domain of Accelerator Physics, which is testified by
intense efforts deployed in this domain by several laboratories worldwide. This
is also the case of CERN, where recently focused efforts have been devoted to
the application of Machine Learning techniques to beam dynamics studies at the
Large Hadron Collider (LHC). This implies a wide spectrum of applications from
beam measurements and machine performance optimisation to analysis of numerical
data from tracking simulations of non-linear beam dynamics. In this paper, the
LHC-related applications that are currently pursued are presented and discussed
in detail, paying also attention to future developments.
