This text for a graduate course on adaptive filtering includes some advanced material of interest to researchers. The text consist of 11 parts: optimal estimation, linear estimation, stochastic gradient algorithms, mean-square performance, transient performance, block adaptive filters, least-squares methods, array algorithms, fast RLS algorithms, lattice filters, and robust filters. Each part contains a series of focused lectures and ends with bibliographic comments, problems, and computer projects with MATLAB solutions. There is a special focus on geometric construction, physical intuition, linear-algebraic concepts, and vector notation. Background material on random variables, linear algebra, and complex gradients is collected in three introductory chapters. Sayed teaches electrical engineering at the University of California-Los Angeles. Annotation ©2008 Book News, Inc., Portland, OR (booknews.com)