CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024
UPOZORNĚNÍ: Jsou dostupné studijní plány pro následující akademický rok.

# Neural Networks 2

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

The second module is oriented first to multi-layer neural networks and next 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.Multi-layer perceptron, universal approximation, backpropagation.

2.Vector space with Minkowski metrics.

3.Metric space of strings.

4.Cluster analysis in vector space.

5.Cluster analysis in metric space.

6.Self-organization, patterns, etalons.

7.SOM as extended cluster analysis.

8.SOM topology, SOM as transformation.

9.Kohonen learning of SOM.

10.SOM learning in metric space.

12. Introduction to deep learning.

Syllabus of tutorials:

1.Multi-layer perceptron, universal approximation, backpropagation.

2.Vector space with Minkowski metrics.

3.Metric space of strings.

4.Cluster analysis in vector space.

5.Cluster analysis in metric space.

6.Self-organization, patterns, etalons.

7.SOM as extended cluster analysis.

8.SOM topology, SOM as transformation.

9.Kohonen learning of SOM.

10.SOM learning in metric space.

12. Introduction to deep learning.

Study Objective:

Knowledge:

Mutli-layer perceptron, cluster analysis, self-organized artificial neural networks, basis of deep learning.

Abilities:

Use of mutli-layer perceptron, use of cluster analysis, use of self-organization, use of deep learning for classification.

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 2023/2024:
Time-table is not available yet
Time-table for summer semester 2023/2024:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-05-27
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