This book covers the fundamentals of adaptive filtering, with a focus on the least mean square (LMS) adaptive filter. It discusses random variables, stochastic processes, vectors, matrices, determinants, discrete random signals, and probability distributions, while delivering a concise introduction to MATLAB®—complete with problems, computer experiments, and over 110 functions and script files. The text not only addresses the basics of the LMS adaptive filter algorithm but also explores the Wiener filter and its applications, details the steepest descent method, and develops the Newton’s algorithm.