  Bose-Einstein condensation (BEC) is a powerful tool for a wide range of
research activities, a large fraction of which are related to quantum
simulations. Various problems may benefit from different atomic species, but
cooling down novel species interesting for quantum simulations to BEC
temperatures requires a substantial amount of optimization and is usually
considered as a hard experimental task. In this work, we implemented the
Bayesian machine learning technique to optimize the evaporative cooling of
thulium atoms and achieved BEC in an optical dipole trap operating near 532 nm.
The developed approach could be used to cool down other novel atomic species to
quantum degeneracy without additional studies of their properties.
