The student will work on machine learning techniques applied to the study of dynamical systems. Specifically, the student will use different Neural Network architectures to approximate the equations governing a given system's evolution. This evolution may be intrinsically random, or we may add randomness to contemplate the possibility of approximating a large dimensional system by a low dimensional one. He will also test this methodology in several applications and report his results to get familiar with the problem and its input data.