Neural Networks 2

Course guarantor:

Lecturer:

Tutor:

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 in vector spaces.

Requirements:

Basic knowledges from linear algebra.

Syllabus of lectures:

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

2. Vector space with Minkowski metrics.

3. Cluster analysis in vector space.

4. Self-organization, patterns, etalons.

5. SOM topology, Kohonen learning of SOM.

6. Traditional principal component analysis.

7. Introduction to deep learning with a focus on convolutional neural networks.

Syllabus of tutorials:

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

2. Vector space with Minkowski metrics.

3. Cluster analysis in vector space.

4. Self-organization, patterns, etalons.

5. SOM topology, Kohonen learning of SOM.

6. Traditional principal component analysis.

7. Introduction to deep learning with a focus on convolutional neural networks.

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:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Applications of Informatics in Natural Sciences (elective course)