  We apply three machine learning strategies to optimize the atomic cooling
processes utilized in the production of a Bose-Einstein condensate (BEC). For
the first time, we optimize both laser cooling and evaporative cooling
mechanisms simultaneously. We present the results of an evolutionary
optimization method (Differential Evolution), a method based on non-parametric
inference (Gaussian Process regression) and a gradient-based function
approximator (Artificial Neural Network). Online optimization is performed
using no prior knowledge of the apparatus, and the learner succeeds in creating
a BEC from completely randomized initial parameters. Optimizing these cooling
processes results in a factor of four increase in BEC atom number compared to
our manually-optimized parameters. This automated approach can maintain
close-to-optimal performance in long-term operation. Furthermore, we show that
machine learning techniques can be used to identify the main sources of
instability within the apparatus.
