  The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment
designed to study neutrino oscillations. Determination of neutrino mass
ordering and precise measurement of neutrino oscillation parameters $\sin^2
2\theta_{12}$, $\Delta m^2_{21}$ and $\Delta m^2_{32}$ are the main goals of
the experiment. A rich physical program beyond the oscillation analysis is also
foreseen. The ability to accurately reconstruct particle interaction events in
JUNO is of great importance for the success of the experiment. In this work we
present a few machine learning approaches applied to the vertex and the energy
reconstruction. Multiple models and architectures were compared and studied,
including Boosted Decision Trees (BDT), Deep Neural Networks (DNN), a few kinds
of Convolution Neural Networks (CNN), based on ResNet and VGG, and a Graph
Neural Network based on DeepSphere. Based on a study, carried out using the
dataset, generated by the official JUNO software, we demonstrate that machine
learning approaches achieve the necessary level of accuracy for reaching the
physical goals of JUNO: $\sigma_E=3\%$ at $E_\text{vis}=1~\text{MeV}$ for the
energy and $\sigma_{x,y,z}=10~\text{cm}$ at $E_\text{vis}=1~\text{MeV}$ for the
position.
