1) Develop different adaptive filtering methods along the two main tracks: steepest descent and Newton's methods.
2) Form a view of two basic functional aspects of adaptive filtering as a signal estimation method and as a parameter estimation method.
3) Settle the significance of autocorrelation and cross correlation matrices in the context of Wiener filter as an adaptive filter.
4) Introduce stochastic gradient estimation methods.
5) Introduce least squares methods.
6) Contrast stochastic gradient estimation based methods and LS methods
7) Introduce performance assesment measures, analyze transient and steady state performances of adaptive algorithms.
8) Relate and contrast transversal AF, IIR AF, recursive AF/Kalman filtering
9) Regarding the design and performance of an adaptive filter, settle the interaction among the role of filter length, step-size and spectral content of filter input signal
10) Study computational aspects, introduce computational varieties of adaptive algorithms.