Pattern Recognition 2003:
Honours Course


Introduction (Please read!)


Assignments:

Your first task is to compile the latex source for Assignment 1. Use the latex command to compile it into a dvi file, which you can view using xdvi. Then use dvips or dvipdf to create a ps or pdf file respectively. If your operating system cannot do this, install one that can. This assignment is already due in week 1, so it's time to start having fun right away!

Assignment 1: Getting started
Assignment 2: K nearest neighbours classifier
Assignment 3: Optimal Bayes classifier
Assignment 4: Parametric Gaussian classifier
Assignment 5: Gaussian mixture model
Assignment 6: Principal component analysis
Assignment 7: Linear discriminant analysis


Resources:

Theory: Jain, Alder. More lecture notes: Gaussians, Gaussian mixture models, PCA/LDA.

LaTeX guide: A not very short introduction to LaTeX

BibTeX guide: BIBTeXing (BibTeX is for bibliographies: you won't use it for this course, but if you become a LaTeX user you will find it useful later.)

Matlab and Octave: Matlab primer Octave homepage

Code and data:
Toy data sets for various experiments
Code fragment for KNN implementation
Data for speaker recognition experiments
Implementation of Gaussian mixture models
Synthetic data for PCA experiment 1
Synthetic data for PCA experiment 2
Facial expression recognition experiment
IO functions for facial expression data


Last updated:23 April 2003
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