Course detail
Artificial Intelligence
FEKT-NUINAcad. year: 2014/2015
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.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
- 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.
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
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
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Schalkoff,R.J.:Artificial Neural Networks. The MIT Press,1997,ISBN 0-07-115554-6 (EN)
Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson, 2008, ISBN 978-0-495-08252-1 (EN)
Recommended reading
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
Neural networks, biological neural units
Model of neurons and paradigm of neuron nets
Multilayer perceptrons, backpropagation algorithm, modified algorithms BP
Neural networks as associative memories, RCE neural network, Kohonen maps
Expert systems, principle, structure
Knowledge reprezentation, logic, production rules
Knowledge reprezentation, semantic nets, frames
Problem solving, type of problems, heuristic
Problem solving methods
Methods of inference
Speech recognition, processing, simulation and synthesis of speech
Speech recognition, methods of pattern recognition, using for instruments
Exercise in computer lab
Teacher / Lecturer
Syllabus
Backpropagation algorithm modelling 1
Backpropagation algorithm modelling 2
Dynamic system modelling by neural network
Sensitivity analysis of neural networks
Pattern recognition by neural networks
Expert systems application