Pattern Recognition

The course is a substitute for:

Pattern Recognition (33RPZ)

Lecturer:

Tutor:

Supervisor:

Department of Cybernetics

Synopsis:

The course gives an introduction to statistical and syntactic pattern recognition. The classification (pattern recognition) problem as risk minimization. Bayesisan and non-Bayesian decision-making. Parametric classifiers. Learning. Parameter estimation. Non-parametric classifiers. The nearest neighbour method. Feed-forward neural nets and the backpropagation algorithm. Vapnik's learning theory. Feature selection. Support Vector Machines. Cluster analysis. Structural risk minimization. Syntactic Pattern Recognition. Languages, grammars, automata. Parsing, syntactic classification.

Requirements:

Syllabus of lectures:

1. The classification (pattern recognition) problem as risk minimization.

2. Bayes decision-making.

3. Non-bayesian recognition methods.

4. Parametric classifiers.

5. Parameter estimation.

6. The Maximum Likelihood method.

7. Non-parametric classifiers. The nearest neighbour method.

8. Neural nets 1. Principles.

9. Neural nets 2. Learning. Testing.

10.Support Vector Machines.

11.Vapnik's learning theory.

12.Adabost.

13.Feature selection.

14.Cluster analysis.

Syllabus of tutorials:

A group of two students designs a classifier for charakter recognition. The data used are from a large, publicly available database. In the first part a simple classifier is trained on a relatively small number of samples. In a second part of the experiment the students explain the differences between a predisted and actual test error on an independent test set.

1. Introduction to MATLAB.

2. The basics of probability theory.

3. Bayes classifier.

4. ML estimation.

5. Perceptron I.

6. Perceptron II.

7. Cluster analysis.

8. Support Vector Machines.

9. Principle component analysis.

10. Project.

11. Project.

12. Project.

13. Project.

14. Project presentation and analysis.

Study Objective:

Study materials:

[1]Duda, Hart, Stork: Pattern Classification. 2nd edition, John Wiley, 2000

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Cybernetics and Measurements - Control Engineering- structured studies (compulsory elective course)
• Cybernetics and Measurements - Artificial Intelligence- structured studies (compulsory elective course)
• Cybernetics and Measurements - Measurement and Instrumentation Systems- structured studies (compulsory elective course)
• Cybernetics and Measurements - Aeronautical Engineering and Control Systems- structured studies (compulsory elective course)