Course detail

Modern Means in Automation

FEKT-KMPAAcad. year: 2018/2019

The course is focused on the use of knowledge systems in automation. In this context, explains the concepts of data, information and knowledge. The lectures are focused on the issue of expert systems, artificial neural networks, machine learning and computer vision.

Learning outcomes of the course unit

Course graduate should be able to:
- explain the differences between the concepts of data, information and knowledge,
- explain the architecture and functionality of expert systems,
- create a base of knowledge for expert system NPS32,
- choose the field of application of expert systems,
- explain the paradigm of multilayer neural networks with backpropagation learning,
- discuss the settings for a parameter, the neural network,
- apply features multi-layered neural network backpropagation learning,
- design own solution of optimization task based on genetic algorithms,
- apply optical information in technical equipment.


The subject knowledge on the secondary school level is required.


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)
Hlaváč V.- Šonka M.: Počítačové vidění. Grada 1992,Praha,ISBN 80-85424-67-3 (CS)
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 1. ACADEMIA 1993,Praha,ISBN 80-200-0496-3 (CS)
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 2. ACADEMIA 1997,Praha,ISBN 80-200-0504-8 (CS)
Berka P. a kol.: Expertní systémy. Skripta, VŠE Praha, 1998. (CS)
Šíma J., Neruda R.: Teoretické otázky neuronových sítí. Matfyzpress, Praha 1996 (CS)
Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson, 2008, ISBN (EN)

Planned learning activities and teaching methods

Techning methods include lectures and computer laboratories. Students have to write 7 assigment 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. Students are tested continuously and i tis possible to get maximum 30 points. The final written exam is rated by 60 points at maximum and the oral exam is rated by 10 points at maximum.

Language of instruction


Work placements

Not applicable.

Course curriculum

1. Automation - relevance, resources, engineering cybernetics.
2. Data, information, knowledge - definition, examples.
3. Expert systems - definitiv, architectural engeneering, theoretical sources, characteristics, inference engine, creation of knowledge base, acguirement of knowledges, proces sof consultation, aplications.
4. Artificial neural networks - definition, neuron, topology, paradigm, multilayer neural network, backpropagation algorithm, activation, characteristics.
5. Industry 4.0 - introdduction to problems.
6.Theory of Inventive Problem Solving - analysis of the object to be improved and formulation of innovative task to be solved, then solving of inventive tasks supported by expert system and information from world patent databases.
7. Computer vision - preprocessing, segmentation, objects description, classification.


The aim of the course is to acquaint the students with modern methods and means in automation. To get knowledge and experience of pattern recognition and using neural networks, expert systems in automation.

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-BK Bachelor's

    branch BK-AMT , 3. year of study, summer semester, 6 credits, optional specialized

  • Programme EEKR-CZV lifelong learning

    branch ET-CZV , 1. year of study, summer semester, 6 credits, optional specialized

Type of course unit



26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

39 hours, compulsory

Teacher / Lecturer