Introduction to adaptive filters, optimal estimation, linear    estimation: normal equation, orthogonality principle, linear models.    Constrained linear estimation: minimum variance unbiased estimation, steepest    descent algorithms, stochastic gradient algorithms: LMS algorithm, normalized    LMS algorithm, RLS algorithm. Steady-state performance of adaptive filters,    transient performance of adaptive filters, block adaptive filters, the    least-squares criterion, recursive least-squares, lattice filters 
         
            Prerequisite: None 
             
            Texts/Reference    Books:            
            
        - Fundamentals of adaptive filtering, A. H. Sayed, Wiley, 2003
 
        - Adaptive filter theory, Simon Haykin, Fourth edition, Pearson, 2012
 
        - Adaptive Signal Processing, Widrow and Stearns, Pearson, 2007
 
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