We optimized control laws for stabilization of two shear flows: a DNS of a flow past a cluster of three rotating cylinders-the fluidic pinball-and the cavity flow experiment. The fluidic pinball is stabilized in increasingly reacher control law spaces employing 9 sensors downstream and 3 actuators (the cylinders). As for the cavity, two regimes are controlled in single-input single-output manner: a single-mode driven regime and a mode-switching regime. Key enablers are automated machine learning algorithms augmented with gradient search: explorative gradient method for the open-loop parameter optimization and a gradientenriched machine learning control (gMLC, [1]) for the feedback optimization. For both plants, the need of feedback for control is demonstrated. gMLC learns the control law significantly faster than previously employed genetic programming control both in DNS and experiment.