Course detail

Artificial Intelligence

FEKT-MPC-UINAcad. year: 2023/2024

The course discusses the basic methods and subdomains of artificial intelligence, namely, machine learning, the structure and activity of knowledge systems, optical information processing, and approaches to the training and application of artificial neural networks.

Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Entry knowledge

The subject knowledge on the Bachelor´s degree level is requested and knowledge about programming MATLAB.

Rules for evaluation and completion of 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.
The computer exercises are compulsory, the properly excused missed computer exercises can be compensate.


The course aims to explain the basic concepts (algorithms) of artificial intelligence, with special emphasis on machine learning, problem solving, knowledge representation, knowledge systems, computer vision, and artificial neural networks.
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.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

MAŘÍK, Vladimír, ŠTĚPÁNKOVÁ, Olga, LAŽANSKÝ, Jiří a kolektiv. Umělá inteligence (1. až 6. díl) Praha: Academia 1993 - 2013. (CS)
RUSESELL, Stuart a NORVIG, Peter. Artificial Intelligence. A Modern Aproach. New Jersey: Prentice Hall 2010. 1132 s. ISBN-13: 978-0-13-604259-4. (EN)

Recommended reading

SONKA, Milan, HLAVAC, Vaclav a BOYLE, Rogert. Image Processing, Analysis and Machine Vision. Toronto: Thomson, 2008. 829 s. ISBN 978-0-495-24438-7. (CS)
DUDA, Richard, HART Peter a STORK David. Pattern Classification. New York: John Wiley & Sons, INC. 2001. 654 s. ISBN 0-471-05669-3. (CS)

Classification of course in study plans

  • Programme MPC-TIT Master's, any year of study, winter semester, elective
  • Programme MPC-EEN Master's, any year of study, winter semester, elective
  • Programme MPC-EAK Master's, any year of study, winter semester, elective

  • Programme MPC-AUD Master's

    specialization AUDM-TECH , 1. year of study, winter semester, compulsory-optional
    specialization AUDM-ZVUK , 1. year of study, winter semester, compulsory-optional

  • Programme MPC-IBE Master's, 2. year of study, winter semester, compulsory-optional

Type of course unit



26 hours, optionally

Teacher / Lecturer


1. Organization of teaching, Intelligence
2. Artificial Intelligence – concepts
3. Artificial neural networks - paradigms, Perceptron
4. Multilayer neural network with Backpropagation learning algorithm
5. Kohonen's self-organizing map, Hopfield network, RCE network
6. Kohonen's self-organizing map, Hopfield network, RCE network
7. Expert Systems - representation of knowledge, problem solving
8. Expert Systems - definition, structure, knowledge base, application
9. Principles of computer vision
10. Principles of computer vision
11. Convolutional neural network
12. Convolutional neural network
13. Intelligent systems


Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer