The lectures this volume collects were designed for an audience having basic knowledge of Functional Analysis and Measure Theory, but not familiar with Probability. The main aim is to give an introduction to Analysis in separable infinite-dimensional Hilbert spaces. The arguments the book deals with are: Gaussian measures, reproducing kernels, Cameron-Martin formula, Brownian motion, Wiener integral, invariant measures, ergodicity, mixing, and Ito-Wiener decomposition.

This volume collects the lecture notes of a twenty-hour introductory course on Differential Stochastic Equations. The lectures were designed for an audience having basic knowledge of Functional Analysis and Measure Theory but not familiar with Probability Theory. The main aim was to popularize the use of Probability among analysts interested in Parabolic Equations. We tried to focus on the idea that ordinary differential stochastic equations play the same role in the theory of second order parabolic equations as deterministic ordinary differential equations do in the study of first order partial differential equations, through the well-known characteristics method.