FEKT-BPC-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.
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 NPSCORE,
- choose the field of application of expert systéme,
- optical information processing devices applied artificial inteligence.
Recommended optional programme components
Recommended or required reading
MAŘÍK, Vladimír, ŠTĚPÁNKOVÁ, Olga, LAŽANSKÝ, Jiří a kolektiv. Umělá inteligence (1. až 6. díl) Praha: Academia 1993 - 2013. (CS)
RUSESELL, Stuart a NORVIG, Peter. Artificial Intelligence. A Modern Aproach. New Jersey: Prentice Hall 2010. 1132 s. ISBN-13: 978-0-13-604259 (EN)
SONKSA, Milan, HLAVAC, Vaclav a BOYLE, Rogert. Image Processing, Analysis and Machine Vision. Toronto: Thomson, 2008. 829 s. ISBN 978-0-495-24438-7. (EN)
DUDA, Richard, HART Peter a STORK David. Pattern Classification. New York: John Wiley & Sons, INC. 2001. 654 s. ISBN 0-471-05669-3. (EN)
Planned learning activities and teaching methods
Techning methods include lectures and computer laboratories. Students have to write a single project 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 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.
Language of instruction
1. Artificial intelligence - definition, history, area
2. Neuroscience - biological information system, neuron, brain, intelligence
3. Artificial neural networks - definitions, paradigms, Perceptron, learning
4. Multilayer neural network with Backpropagation learning algorithm
5. Kohonen's self-organizing map, Hopfield network, RCE network
6. public holiday 2019
7. Principles of computer vision
8. Principles of computer vision
9. Expert Systems - representation of knowledge, problem solving
10. Expert Systems - definition, structure, knowledge base, application
11. Convolutional neural network
12. Convolutional neural network
13. Intelligent systems
The course aims to explain the basic concepts (algorithms) of artificial intelligence, with special emphasis on machine learning, problem solving, knowledge representation, knowledge systems, computer vision, and artificial neural networks.
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 BPC-AUD Bachelor's
- Programme BPC-EKT Bachelor's, any year of study, winter semester, 5 credits, elective
- Programme BPC-IBE Bachelor's, any year of study, winter semester, 5 credits, elective
- Programme BPC-MET Bachelor's, any year of study, winter semester, 5 credits, elective
- Programme BPC-SEE Bachelor's, any year of study, winter semester, 5 credits, elective
- Programme BPC-TLI Bachelor's, any year of study, winter semester, 5 credits, elective
- Programme BPC-AMT Bachelor's, 3. year of study, winter semester, 5 credits, compulsory