  The complex non-linear processes in multi-dimensional parameter spaces, that
are typical for an accelerator, are a natural application for machine learning
algorithms. This paper reports on the use of Bayesian optimization for the
optimization of the Injection Beam Line (IBL) of the Cooler Synchrotron storage
ring COSY at the Forschungszentrum J\"ulich, Germany. Bayesian optimization is
a machine learning method that optimizes a continuous objective function using
limited observations. The IBL is composed of 15 quadrupoles and 28 steerers.
The goal is to increase the beam intensity inside the storage ring. The results
showed the effectiveness of the Bayesian optimization in achieving
better/faster results compared to manual optimization.
