Course detail

Artificial Intelligence

FEKT-NUINAcad. year: 2018/2019

The aim of the course is to deepen knowledges and application of artificial intelligence methods. Artificial intelligence – definition, trends. Artificial neural networks, neural networks paradigms, method of backpropagation learning, Kohonen self-organizing maps, Hopfield network, RCE neural network. Knowledge-based systems, knowledge representation, problem solving, structure and activities of expert systems. Optical information processing resources of artificial inteligence. Intelligent robot.

Learning outcomes of the course unit

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.

Recommended optional programme components

Not applicable.

Recommended or required reading

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)

Planned learning activities and teaching methods

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

Assesment methods and criteria linked to learning outcomes

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.

Language of instruction


Work placements

Not applicable.

Course curriculum

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


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.

Specification of controlled education, way of implementation and compensation for absences

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

Classification of course in study plans

  • Programme EEKR-MN Master's

    branch MN-TIT , 1. year of study, winter semester, 5 credits, compulsory
    branch MN-KAM , 2. year of study, winter semester, 5 credits, compulsory
    branch MN-EEN , 2. year of study, winter semester, 5 credits, compulsory

Type of course unit



39 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, optionally

Teacher / Lecturer