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

Neural Networks 2

Login to KOS for course enrollment Display time-table
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.

11.Traditional principal component analysis.

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.

11.Traditional principal component analysis.

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
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet3023106.html