English

Not applicable.

Course graduate should be able to:
- explain the concept of artificial intelligence from the perspective of its application in technical equipment,
- explain the paradigm for artificial neural network: perceptron, multilayer neural network backpropagation learning, Kohonen self-organizing maps, Hopfield network, RCE neural network,
- discuss and verify the settings of individual parameters of the selected neural network,
- assess the scope of application of artificial neural network,
- explain the architecture and functionality of knowledge systéme,
- create a base of knowledge for expert system NPS32,
- choose the field of application of expert systéme,
- optical information processing devices applied artificial inteligence.

The subject knowledge on the Bachelor´s degree level is requested.

Not applicable.

Techning methods include lectures and computer laboratories. Students have to write a single project during the course.

Condition of petting full credit is absolut (100%) attendance in obligatory parts of lessons - the computer exercises and obtaining at least 15 points. Students are tested continuously and i tis possible to get maximum 20 points. The final written exam is rated by 70 points at maximum and the oral exam is rated by 10 points at maximum.

1. Artificial intelligence - definition, history, area
2. Neuroscience - biological information system, neuron, brain, intelligence
3. Principles of computer vision
4. Artificial neural networks - definitions, paradigms
5. Perceptron, learning
6. Multilayer neural network with Backpropagation learning algorithm
7. Kohonen's self-organizing map, Hopfield network, RCE network
8. Convolutional neural network
9. Artificial Intelligence and practice
10. Representation of knowledge, problem solving
11. Expert Systems - definition, structure, knowledge base, application
12. Intelligent robot

Not applicable.

The aim of this course is to provide students with a basic orientation in key algorithms and artificial inteligence, emphasis is placed on the field of artificial neural networks, knowledge systems and computer vision.

The computer exercises are compulsory, the properly excused missed computer exercises can be compensate.

Not applicable.

Not applicable.

Kasabov,N.K.: Foundations of Neural Networks, Fuzzy systems and Knowledge Engineering.The MIT Press,1996,ISBN 0-262-11212-4 (EN)
Schalkoff,R.J.:Artificial Neural Networks. The MIT Press,1997,ISBN 0-07-115554-6 (EN)
Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson, 2008, ISBN 978-0-495-08252-1 (EN)

Not applicable.