doc. Ing. Václav Jirsík, CSc.

Department of Control and Instrumentation (UAMT)

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.

Not applicable.

Kasabov,N.K.: Foundations of Neural Networks, Fuzzy systems and Knowledge Engineering.The MIT Press,1996,ISBN 0-262-11212-4 (ET)
Schalkoff,R.J.:Artificial Neural Networks. The MIT Press,1997,ISBN 0-07-115554-6 (EN)
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)
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 3. ACADEMIA 2001,Praha,ISBN 80-200-0472-6 (CS)
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 4. ACADEMIA 2003,Praha,ISBN 80-200-1044-0 (EN)
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 978-0-495-08252-1 (EN)

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.

Czech

Not applicable.

1. Artificial intelligence-definition, history, area
2. Biological information system, neuron, brain, inteligence
3. Artificial neural networks-modelling and properties of neural networks, paradigma
4. Artificial neural networks-perceptron
5. Artificial neural networks-multilayer neural network with backpropagation learning algorithm
6. Artificial neural networks-properties of multilayer neural network
7. Artificial neural networks-Kohonen self-organizing maps
8. Artificial neural networks-Hopfield network
9. Artificial neural networks-RCE neural network
10. Knowledge-based systems-knowledge representation, problem solving
11. Knowledge-based systems-structure and the activity of expert systems
12. Principles of computer vision
13. 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.

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

• Programme EEKR-M1 Master's

  branch M1-TIT , 1. year of study, winter semester, 5 credits, optional interdisciplinary
  

• Programme EEKR-M Master's

  branch M-TIT , 1. year of study, winter semester, 6 credits, optional interdisciplinary
  

• Programme EEKR-M1 Master's

  branch M1-KAM , 2. year of study, winter semester, 5 credits, compulsory
  

• Programme EEKR-M Master's

  branch M-KAM , 2. year of study, winter semester, 6 credits, compulsory
  

• Programme EEKR-M1 Master's

  branch M1-EEN , 2. year of study, winter semester, 6 credits, optional interdisciplinary
  

• Programme EEKR-M Master's

  branch M-EEN , 2. year of study, winter semester, 6 credits, optional interdisciplinary
  

• Programme EEKR-CZV lifelong learning

  branch ET-CZV , 1. year of study, winter semester, 5 credits, compulsory
  

26 hours, compulsory

Ing. Daniel Davídek
doc. Ing. Václav Jirsík, CSc.
Ing. Jan Klečka, Ph.D.
Ing. Petr Nováček