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 A,B and C
Appendices D and E