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.
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. 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
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
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)
Recommended reading
SONKA, Milan, HLAVAC, Vaclav a BOYLE, Rogert. Image Processing, Analysis and Machine Vision. Toronto: Thomson, 2008. 829 s. ISBN 978-0-495-24438-7. (CS)
Classification of course in study plans
- Programme MPC-IBE Master's 2 year of study, winter semester, compulsory-optional
- Programme MPC-AUD Master's
specialization AUDM-TECH , 1 year of study, winter semester, compulsory-optional
specialization AUDM-ZVUK , 1 year of study, winter semester, compulsory-optional - Programme MPC-TIT Master's 0 year of study, winter semester, elective
- Programme MPC-EEN Master's 0 year of study, winter semester, elective
- Programme MPC-KAM Master's 0 year of study, winter semester, elective
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Inteligence - biological information system, neuron, brain, data, information, knowledge.
3. Artificial neural networks - paradigma, learning, perceptron.
4. Artificial neural networks - multilayer neural network with backpropagation learning algorithm.
5. Artificial neural networks - Kohonen self-organizing maps.
6. Artificial neural networks - Hopfield network, RCE neural network.
7. Computer vision - preprocessing, classification.
8. Convolutional neural network.
9. Knowledge-based systems-knowledge representation, problem solving.
10. Knowledge-based systems-structure and the activity of expert systems.
11. Intelligent robot.
Exercise in computer lab
Teacher / Lecturer
Syllabus
2. Projekt (práce doma)
3. Úvod, projekty – zadání
4. Umělé neuronové sítě
5. Umělé neuronové sítě
6. Umělé neuronové sítě
7. Umělé neuronové sítě
8. Počítačové vidění
9. Expertní systémy + zadání
10. Projekt 1 – Referát
11. Projekt 1 – Referát
12. Expertní systémy
13. Expertní systémy