Learning and optimization tasks can be solved by modern ensemble filtering methods. A mathematical formulation as inverse problem allows to apply ensemble Kalman filter methods to obtain a gradient free, converging algorithm for general inverse problems. The analysis of the method is based on the large ensemble limit using kinetic theory. Stabilization of the method as well as numerical results will be shown in this talk.