# Signal Processing Course

Copyright (c) 2009 William D Penny.
Permission is granted to copy, distribute and/or modify this document
under the terms of the GNU Free Documentation License, Version 1.2
or any later version published by the Free Software Foundation

This lecture course was given at the Institute of Neurology, University College
London in the 1999/2000 academic year.
You can download all the notes in one go (178 pages)
as a
paper-saving version ) (two text-pages per printed page)
or a normal version ).
Alternatively, you can download a lecture at a time.
You may wish to refer to the table of
contents .

Here's a PDF version of the lecture notes

You may also be interested in links to relevant
software packages.

### Part I: Fundamentals

1.
Statistics

2.
Linear Algebra

3.
Multivariate Analysis

4.
Information Theory

### Part II: Stationary Models

5.
Fourier Methods

6.
Stochastic Processes

7.
Multiple Time Series

8.
Subspace Methods

9.
Nonlinear Methods

### Part III: Nonstationary Models

10.
Bayesian Methods

11.
Kalman Filters

12.
EM algorithms

13. Hidden Markov Models

14. Wavelets

15. Independent Component Analysis

### Appendices

Appendices A,B and C

Appendices D and E

References