  The lack of tangible evidence for non-gravitational interactions between dark
and visible sectors drives the need for exploring new avenues of inferring dark
matter properties through purely gravitational probes. In particular,
addressing small-scale distribution of dark matter could lead to valuable new
insights into its particle nature, either confirming predictions of the
collisionless cold dark matter hypothesis or favouring models with suppressed
small-scale matter power spectrum. In this work we propose a novel machine
learning approach for constraining the abundance of galactic dark matter
subhaloes through the analysis of Milky Way's stellar field that has been only
recently mapped with sufficient coverage thanks to the Gaia mission. Our method
is based on convolutional neural networks which represent a powerful tool for
identifying characteristic perturbations in spatial maps of stellar number
density and velocity distribution moments. For generating the training data we
develop a robust and computationally efficient algorithm, capable of generating
mock stellar fields from an arbitrary underlying phase-space distribution of
stars. By preforming a preliminary study of the outlined approach on synthetic
datasets we demonstrate that sensitivities down to (or even below) $10^8
M_\odot$ could be reached. Furthermore, our results show that the accuracy of
the advocated technique crucially depends on the kinematic properties of mapped
stars and could be further improved by applying it to abundant stellar
populations with particularly low velocity dispersion, such as the galactic
thin disc stars.
