Machine Learning (ML) offers the capability to model complex behaviors from data alone, which we apply here to the challenge of a relatively simple problem, mixing by cutting-and-shuffling. In general, mixing can be thought of as the transition from more segregated to less segregated states . Mixing problems for fluids, solids, and fluids with solids are ubiquitous , from the viscous aspects of food processing to the preparation of impregnated powder catalysts . Mixing between species requires contact between them, which is measured by the intermaterial contact area, and stretching and folding (chaotic advection) can increase the intermaterial contact area exponentially . Dynamical systems approaches have been the tools of choice to study fluid mixing for decades. Chaotic mixing , in particular, is fully captured through an understanding of dynamical systems, which goes back to the work of Poincaré in 1887 .