This coming semester will be my last. I have 1 class left for my MS EE (Communication emphasis) to take, while working full time.
I was considering between the following 2:
Adaptive Signal Processing
Description: Weiner filtering, linear prediction, method of steepest descent, stochastic gradient algorithms, recursive least-squares (RLS), fast RLS, RLS with systolic arrays, QRD-least squares methods, blind deconvolution.
-or-
Mathematical Pattern Recognition
Description: Distribution free classification, discriminant functions, training algorithms; statistical classification, parametric and nonparametric techniques, potential functions; non-supervised learning.
They both sound hard. Suggestions? Insights?
I was considering between the following 2:
Adaptive Signal Processing
Description: Weiner filtering, linear prediction, method of steepest descent, stochastic gradient algorithms, recursive least-squares (RLS), fast RLS, RLS with systolic arrays, QRD-least squares methods, blind deconvolution.
-or-
Mathematical Pattern Recognition
Description: Distribution free classification, discriminant functions, training algorithms; statistical classification, parametric and nonparametric techniques, potential functions; non-supervised learning.
They both sound hard. Suggestions? Insights?
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