Course contents: Fundamentals for adaptive systems; mean-square estimation, Wiener filters. Introduction to adaptive structures and the least squares method. Optimization techniques: Gradient and Newton methods. LMS (least mean squares), RLS (recursive least squares). Analysis of adaptive algorithms: Learning curve, convergence, stability, excess mean square error, misadjustment. Applications in telecommunication systems (channel equalization, echo cancellation).

Assessment: Examination for both theory (70%) and laboratory practice (30%). Theory: written exams at the end of the semester. It is possible that home assignments will be given, which will contribute to the final grade with a percentage ranging between 10% and 20%. Laboratory practice: Written exams at the end of the semester or home assignments or both.