Course detail

Artificial Intelligence

FEKT-MPC-UINAcad. year: 2020/2021

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.

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 and knowledge about programming MATLAB.


Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

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)
SONKSA, 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)

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: history, definition, and subdomains
2. Intelligence: the biological information system; neuron; brain; data; information; knowledge
3. Machine learning: the basic concepts and methods
4. Problem solving and knowledge representation: introduction and fundamental techniques
5. Knowledge-based systems: the structure and activity of expert systems
6. Computer vision
7. Artificial neural networks: the perceptron; backpropagation learning algorithm; convolutional neural networks


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.

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 MPC-TIT Master's, any year of study, winter semester, 5 credits, optional
  • Programme MPC-KAM Master's, any year of study, winter semester, 5 credits, optional
  • Programme MPC-EEN Master's, any year of study, winter semester, 5 credits, optional

  • Programme MPC-AUD Master's

    specialization AUDM-TECH , 1. year of study, winter semester, 5 credits, compulsory-optional
    specialization AUDM-ZVUK , 1. year of study, winter semester, 5 credits, compulsory-optional

  • Programme MPC-IBE Master's, 2. year of study, winter semester, 5 credits, compulsory-optional

Type of course unit



26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer