Computational Intelligence, SS08
2 VO 442.070 + 1 RU 708.070 last updated:
Course Notes (Skriptum)
Online Tutorials
Introduction to Matlab
Neural Network Toolbox
OCR with ANNs
Adaptive Filters
VC dimension
Gaussian Statistics
PCA, ICA, Blind Source Separation
Hidden Markov Models
Mixtures of Gaussians
Automatic Speech Recognition
Practical Course Slides
Animated Algorithms
Interactive Tests
Key Definitions
Literature and Links

Gaussian Statistics and Unsupervised Learning

A Tutorial for the Course Computational Intelligence


This tutorial presents the properties of the Gaussian probability density function. Subsequently, supervised and unsupervised pattern recognition methods are treated. Supervised classification algorithms are based on a labeled data set. The knowledge about the class membership of this training data set is used for the classification of new samples. Unsupervised learning methods establish clusters from an unlabeled training data set. Clustering algorithms such as the $ K$-means, the EM (expectation-maximization) algorithm, and the Viterbi-EM algorithm are presented.


To make full use of this tutorial you have to
  1. download the file which contains this tutorial in printable format (PDF and ps.gz) and the accompanying Matlab programs.
  2. Unzip which will generate a subdirectory named Gaussian/matlab where you can find all the Matlab programs.
  3. Add the path Gaussian/matlab to the matlab search path, for example with a command like addpath('C:\Work\Gaussian\matlab') if you are using a Windows machine, or by using a command like addpath('/home/jack/Gaussian/matlab') if you are on a Unix/Linux machine.


This tutorial is based on
  • EPFL lab notes ``Introduction to Gaussian Statistics and Statistical Pattern Recognition'' by Hervé Bourlard, Sacha Krstulovic, and Mathew Magimai-Doss.