Choices between two signal processing classes

[chingkui]

I am interested in DSP, for the coming semester, I can only take one signal processing class, and I have two choices that I am not exactly sure what they really are:

1) Statistical Signal Processing:
Detection theory and hypothesis testing. Introduction to estimation theory. Properties of estimators, Gauss-Markov theorem. Estimation of random variables: conditional mean estimates, linear minimum mean-square estimation, orthogonality principle, Wiener and Kalman filters. Adaptive filtering. LMS algorithm: properties and applications.

2) Adaptive Signal Processing:
Theory of adaptation with stationary signals; performance measures. LMS, RLS algorithms.

From the description, I am not really sure what these two courses are really about, for example, what problems motivate the study of the two, what important applications each one have, and the usefulness of each.

I would appreciate if someone could offer me some insight into the two areas. I could only choose one. Thank you very much.

 

[chroot]

Statistical signal processing is essentially a math class. You're not going to learn any practical DSP in that class, but, often, it's a prereq for a real DSP class. The adaptive signal processing class is almost certainly designed to be taken after some other basic signal processing classes.

- Warren

 

[jbusc]

If it were up to me, the 1st one. You will learn about random variables, Markov processes, and general estimation theory - things which have unlimited application in engineering outside of DSP as well.

The second sounds way too focused and specific.