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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025

Neural Networks 1

The course is not on the list Without time-table
Code Completion Credits Range Language
818NES1 Z 2 1+1 Czech
Garant předmětu:
Lecturer:
Tutor:
Supervisor:
Department of Software Engineering
Synopsis:

Mathematical analysis, model theory and biological context are used for construction of simple models of neural structures. The models are able to learn from pattern sets and their structures and parameters are subjects of optimization.

Requirements:

Basic knowledges from linear algebra.

Syllabus of lectures:

1.Biological neural networks and their models.

2.Artificial neural networks, basic terms.

3.ANN topology, acyclic and hierarchic networks

4.Bipolar perceptron as switching element.

5.Logical function as perceptron network.

6.Hebb learning, LSQ learning, pseudoinversion, OLAM.

7.Robust learning principles, pruning.

8.Rosenblatt learning, Widrow delta learning.

9.Non-linear preprocessing and Cover theorem.

10.Smooth perceptron, delta rule, stochastic gradient method.

11. Support Vector Machine

Syllabus of tutorials:

1.Biological neural networks and their models.

2.Artificial neural networks, basic terms.

3.ANN topology, acyclic and hierarchic networks

4.Bipolar perceptron as switching element.

5.Logical function as perceptron network.

6.Hebb learning, LSQ learning, pseudoinversion, OLAM.

7.Robust learning principles, pruning.

8.Rosenblatt learning, Widrow delta learning.

9.Non-linear preprocessing and Cover theorem.

10.Smooth perceptron, delta rule, stochastic gradient method.

11. Support Vector Machine

Study Objective:

Knowledge:

Elements of artificial neural networks.

Abilities:

Representation of logical function as perceptron network, use of algorithms for weights calculation of perceptron network, use of support vector machine.

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:
Data valid to 2024-06-16
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