| Course Unit Code | 450-4049/04 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Optional |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | |
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| Semester when the Course Unit is delivered | Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | English |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| CER275 | prof. Ing. Martin Černý, Ph.D. |
| KUB631 | Ing. Jan Kubíček, Ph.D. |
| REY0014 | Karla Miriam Reyes Leiva |
| GUP0004 | Ankit Gupta |
| Summary |
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| The course is primarily focused on gaining knowledge of the function and potential application of artificial intelligence methods in the context of their programme of study. The course will introduce students to selected artificial intelligence methods and focus on their practical implementation in their engineering practice. Core areas include fuzzy logic and expert systems, cluster analysis and optimization methods, neural networks, decision trees and forests, machine learning methods without neural networks, hybrid and special machine learning methods, and the use of generative artificial intelligence. |
| Learning Outcomes of the Course Unit |
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| Students will learn about the basics of the science of artificial intelligence, learn about its tools in engineering application areas with respect to the teaching of the subject in the given study program. They will learn about the methods of synthesis of simple artificial intelligence systems. Students will be able to make practical use of artificial intelligence tools, design a fuzzy expert system, an artificial neural network or an optimization algorithm with respect to applications in their field of study. |
| Course Contents |
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Lectures
1. Principles and methods of artificial intelligence. Methods of computer knowledge representation and language modelling. Basics of fuzzy mathematics and fuzzy logic
2. Fuzzy expert systems
3. Fuzzy models and ANFIS
4. Data classification: basic methods, principles and applications Hierarchical and non-hierarchical cluster analysis methods.
5. and 6. Neural networks: basic principles, topologies, network types and applications for classification and prediction.
7. Optimization methods and applications.
8. Decision trees and forests, random trees.
9. a 10. Machine learning methods without neural networks
11. Special machine learning methods: reinforcement learning, federated learning, transfer learning, multi-source and multi-view learning
12. Hybrid methods
13. Generative artificial intelligence and its application in engineering practice.
Computer exercises
1. Mathematical applications of fuzzy mathematics.
2. Design and implementation of fuzzy expert systems.
3. Application of fuzzy modeling on real examples.
4. Implementation of selected classification algorithms in the context of engineering applications.
5. Design and implementation of neural networks in MATLAB environment for solving classification and prediction tasks.
6. Application of optimization techniques.
7. Implementation of cluster analysis methods for biomedical data segmentation and classification.
8. Implementation of decision tree methods
9. Implementation of machine learning methods without neural networks
10. implementation of selected special machine learning methods in engineering applications
11. Implementation of selected hybrid methods in engineering applications
12. Experimentation with generative artificial intelligence
13. Credit test
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| Recommended or Required Reading |
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| Required Reading: |
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RUSSEL,S., NORVIG,P.: Artificial Intelligence, Prentice-Hall, Inc., 2003, ISBN 0-13-080302-2
LUGER,G.F., STUBBLEFIELD,W.A.: Artificial Intelligence, The Benjamin/Cummings Publishing Company, Inc., 2009, ISBN-13: 978-0-321-54589-3 ISBN-10: 0-321-54589-3
ZIMMERMANN,H.J. Fuzzy Set Theory - and Its Applications. Kluwer Academic Publishers, 2001. ISBN-13: 978-0792374350
HUDSON, D. L. a M. E. COHEN. Neural networks and artificial intelligence for biomedical engineering. New York: Institute of Electrical and Electronics Engineers, c2000. ISBN 978-0780334045.
AGAH, Arvin. Medical applications of artificial intelligence. Boca Raton: CRC Press/Taylor & Francis Group, 2014. ISBN 9781439884331.
SILER, William a BUCKLEY, James J. Fuzzy expert systems and fuzzy reasoning. Hoboken: John Wiley, 2005. ISBN 0-471-38859-9.
AKAY, Metin (ed.). Nonlinear biomedical signal processing. Volume I, Fuzzy logic, neural networks, and new algorithms. IEEE Press series on biomedical engineering. New York: IEEE Press, c2000. ISBN 0-7803-6011-7.
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POKORNÝ,M.,SROVNAL,V. Systémy s umělou inteligencí - Učební text a návody do cvičení. CZ.1.07/2.2.00/15.0113. VŠB - Technická univerzita Ostrava. Ostrava. 2012
MARČEK, Dušan. Neuronové sítě a fuzzy časové řady s aplikacemi v ekonomice. Opava: Slezská univerzita, 2002. ISBN 80-7248-157-6.
SILER, William a BUCKLEY, James J. Fuzzy expert systems and fuzzy reasoning. Hoboken: John Wiley, 2005. ISBN 0-471-38859-9.
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| Recommended Reading: |
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C. R. REEVES, J. E. ROW,. Genetic Algorithms: Principles and Perspectives. Kluwer Academic Publishers, New York, 2002.
GRAUPE,D. Principles of Artificial Neural Netvorks. World Scientific. 2013. ISBN: 978-981-4522-73-1
BEGG, Rezaul., Daniel T. H. LAI a Marimuthu. PALANISWAMI. Computational intelligence in biomedical engineering. Boca Raton: CRC Press, c2008. ISBN 9780849340802.
SHUKLA, Anupam a Ritu TIWARI. Intelligent medical technologies and biomedical engineering: tools and applications. Hershey, PA: Medical Information Science Reference, c2010. ISBN 1615209778.
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VOLNÁ,E. Neuronové sítě. Ostravská univerzita, Ostrava. 2008
HYNEK,J. Genetické algoritmy a genetické programování. Grada, 2008. ISBN: 978-80-247-2695-3
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| Planned learning activities and teaching methods |
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| Lectures, Tutorials, Experimental work in labs |
| Assesment methods and criteria |
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| Tasks are not Defined |