Course overview and schedule:
Syllabus
Homework Instructions:
1. Batch LMS ARMA modeling
2. Linear System Responses
3. Source separation
Data files:
Tutorials:
Matlab tutorials: several courses from Mathworks,
and Open Course Work from MIT
Linear Algebra: Open Course Work from MIT
Digital Signal Processing: graphic
demonstrations in matlab
Andrew Ng's lectures on machine learning:
videos and
course page.
Geoffrey Hinton's neural networks for machine learning: Coursera class
Digital signals processing: Coursera class
Tools:
A fair number of good algorithms: netlab (Logistic regression, mixture EM, PCA, ...)
Lectures:
1. Introduction
2. Linear Systems, Impulse Response, ARMA
filter
errata: to model an i/o relationship with an AR filter
use: IIR adaptive
filter or see
more complete discussion.
3. z-transform, fourier
transform, filter design, circular convolution, sampling
theorem
4. Random Variables
5. Jointly Distributed Random Variables
6. Stochastic Processes, Power Spectrum, ARMA model, linear prediction
7. Probablistic Estimation, Harmonic process
8. Linear discrimnation, Single trial detection in EEG/MEG
9. Linear Mixtures, ICA, PCA
10. Kalman Filter, Hidden Markov Model