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Lecturer(s)
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Vomáčková Eliška, Doc. Dr. Ing.
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Course content
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Motivations for the development of artificial neural networks, history of the development of artificial neural networks. Basic functions of human brain, principles of biological neural networks. Basic terms, models of the neuron, basic types of neural networks: multilayer networks, recurrent networks, feedforward networks. Algorithms for neural network learning, supervised learning, unsupervised learning. Algorithm backpropagation, self-organizing networks. Principles of the function of neural networks. Associative memories. Hopfield network. Recognition, separability. Examples of the application of neural networks.
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Learning activities and teaching methods
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Self-study of literature, Textual studies, Lecture with visual aids, Practicum
- Contact hours
- 39 hours per semester
- Preparation for an examination (30-60)
- 40 hours per semester
- Presentation preparation (report) (1-10)
- 10 hours per semester
- Graduate study programme term essay (40-50)
- 45 hours per semester
- Preparation for comprehensive test (10-40)
- 23 hours per semester
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| prerequisite |
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| Knowledge |
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| Students should have basic knowledge of mathematics. |
| learning outcomes |
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| The graduates will chiefly be able to: - define terms of neural networks, - describe various types of neural networks, - list the areas where artificial neural networks can be used, - explain function of basic types of artificial neural networks. |
| teaching methods |
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| Lecture with visual aids |
| Practicum |
| Textual studies |
| Self-study of literature |
| assessment methods |
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| Combined exam |
| Individual presentation at a seminar |
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Recommended literature
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Fanta, Jiří. Neuronové sítě ve společenských vědách. Vyd. 1. Praha : Karolinum, 2000. ISBN 80-246-0175-3.
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Novák, Mirko. Umělé neuronové sítě : teorie a aplikace. 1. vyd. Praha : C.H. Beck, 1998. ISBN 80-7179-132-6.
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