Computational Intelligence, SS08
2 VO 442.070 + 1 RU 708.070 last updated:
Course Notes (Skriptum)
Online Tutorials
Practical Course Slides
Animated Algorithms
Interactive Tests
Key Definitions
Literature and Links


Aims and objectives of the course

Knowledge of the most important concepts and methods form the area adaptive filters, machine learning and neural networks.

Covered Topics

  1. Introduction to Machine Learning
  2. Learning Algorithms for Neural Networks
  3. Learning Algorithms and Signal Processing: adaptive Filters
  4. Algorithm independent Machine Learning
  5. Practical Classification Algorithms
  6. Unsupervised Learning
  7. Hidden Markov Models
  8. Application of HMMs to Speech Recognition

Übungen / Tutorials

  1. Getting started with Matlab
  2. Introduction to the Matlab Neural Network Toolbox (NNT)
  3. Using the NNT to solve a digit classification task
  4. Adaptive filters
  5. Software for Classification and Regression
  6. Comparing classification algorithms
  7. Gaussian Statistics
  8. PCA, ICA, Blind Source Separation
  9. Hidden Markov Models
  10. Mixtures of Gaussians
  11. Automatic Speech Recognition


There are no particular courses which must be taken as prerequisites for this course. Although there will be an introduction to MATLAB in the beginning of the exercises, it is recommended to have already some basic knowledge and experience in it. We also assume elementary mathematical knowledge in probability theory, statistics, analysis and calculus.

Bibliography and Teaching aids