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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2020/2021

Neural Networks 2

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Code Completion Credits Range Language
818NES2 Z 2 1+1 Czech
Lecturer:
Kateřina Horaisová (guarantor)
Tutor:
Kateřina Horaisová (guarantor)
Supervisor:
Department of Software Engineering
Synopsis:

The second module is oriented to self-organized artificial neural networks. The biological context, cluster analysis and principal component analysis are used for self-organized artificial neural network realization. Self-organization is discussed both in vector and metric spaces.

Requirements:

Basic knowledges from linear algebra.

Syllabus of lectures:

1.Self-organization, patterns, etalons.

2.Vector space with Minkowski metrics.

3.Metric space of strings.

4.Cluster analysis in vector space.

5.Cluster analysis in metric space.

6.SOM as extended cluster analysis.

7.SOM topology, SOM as transformation.

8.Kohonen learning of SOM.

9.SOM learning in metric space.

10.Traditional principal component analysis.

11.Oja neuron, Oja learning.

12.APEX and GHA networks.

13.Kernel PCA technique.

Syllabus of tutorials:

1.Self-organization, patterns, etalons.

2.Vector space with Minkowski metrics.

3.Metric space of strings.

4.Cluster analysis in vector space.

5.Cluster analysis in metric space.

6.SOM as extended cluster analysis.

7.SOM topology, SOM as transformation.

8.Kohonen learning of SOM.

9.SOM learning in metric space.

10.Traditional principal component analysis.

11.Oja neuron, Oja learning.

12.APEX and GHA networks.

13.Kernel PCA technique.

Study Objective:

Knowledge:

Cluster analysis, self-organized artificial neural networks.

Abilities:

Use of cluster analysis, use of self-organization.

Study materials:

Compulsory literature:

[1] J. Šíma, R. Neruda: Teoretické otázky neuronových sítí, Matfyzpress, Praha, 1996.

[2] M. Šnorek: Neuronové sítě a neuropočítače, ČVUT, Praha 2002

Recommended literature:

[3] S. Haykin: Neural Networks, Macmillan, New York, 1994.

[4] L.V. Fausett: Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall, New Jersey, 1994.

Note:
Time-table for winter semester 2020/2021:
Time-table is not available yet
Time-table for summer semester 2020/2021:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2020-10-22
For updated information see http://bilakniha.cvut.cz/en/predmet3023106.html