FEKT-LUINAcad. year: 2015/2016
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.
Recommended optional programme components
Recommended or required reading
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 (CS)
Schalkoff,R.J.:Artificial Neural Networks. The MIT Press,1997,ISBN 0-07-115554-6 (EN)
Kasabov,N.K.: Foundations of Neural Networks, Fuzzy systems and Knowledge Engineering.The MIT Press,1996,ISBN 0-262-11212-4 (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)
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
Work of students is evaluated during study by tests in exercises and one control test. They can obtain maximum 30 points by these tests during semester.
Final examination is evaluated by 70 points at maximum and the oral exam is rated by 10 points at maximum.
Language of instruction
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.
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-ML Master's
branch ML-KAM , 2. year of study, winter semester, 6 credits, compulsory
- Programme EEKR-ML1 Master's
branch ML1-KAM , 2. year of study, winter semester, 6 credits, compulsory
- Programme EEKR-CZV lifelong learning
branch ET-CZV , 1. year of study, winter semester, 6 credits, compulsory