Neural Networks 2
Code  Completion  Credits  Range  Language 

818NES2  Z  2  1+1  Czech 
 Garant předmětu:
 Lecturer:
 Tutor:
 Supervisor:
 Department of Software Engineering
 Synopsis:

The second module is oriented first to multilayer neural networks and next to selforganized artificial neural networks. The biological context, cluster analysis and principal component analysis are used for selforganized artificial neural network realization. Selforganization is discussed both in vector and metric spaces.
 Requirements:

Basic knowledges from linear algebra.
 Syllabus of lectures:

1.Multilayer 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.Selforganization, 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.Multilayer 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.Selforganization, 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:
Mutlilayer perceptron, cluster analysis, selforganized artificial neural networks, basis of deep learning.
Abilities:
Use of mutlilayer perceptron, use of cluster analysis, use of selforganization, 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 timetable has been prepared for this course
 The course is a part of the following study plans:

 Applications of Informatics in Natural Sciences (elective course)