Materials are available at https://lms.vsb.cz/?lang=en.
Consultation through MS Teams.
Applied Artificial Intelligence Methods
| Type of study | Follow-up Master |
|---|---|
| Language of instruction | Czech |
| Code | 450-4049/05 |
| Abbreviation | AUI |
| Course title | Applied Artificial Intelligence Methods |
| Credits | 5 |
| Coordinating department | Department of Cybernetics and Biomedical Engineering |
| Course coordinator | prof. Ing. Martin Černý, Ph.D. |
Subject syllabus
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
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
E-learning
Literature
RICHTER, Michael M.; PAUL, Sheuli; KËPUSKA, Veton a SILAGHI, Marius. Signal processing and machine learning with applications. Cham, Switzerland: Springer, [2022]. ISBN 978-3-319-45371-2.
ARIF, Tariq M. a RAHIM, Md Adilur. Deep learning for engineers. Boca Raton: CRC Press, Taylor & Francis Group, 2024. ISBN 978-1-03-250473-5.
ARIF, Tariq M. a RAHIM, Md Adilur. Deep learning for engineers. Boca Raton: CRC Press, Taylor & Francis Group, 2024. ISBN 978-1-03-250473-5.
Advised literature
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 .
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.
SILER, William a BUCKLEY, James J. Fuzzy expert systems and fuzzy reasoning. Hoboken: John Wiley, 2005. ISBN 0-471-38859-9 .
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 .
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.
SILER, William a BUCKLEY, James J. Fuzzy expert systems and fuzzy reasoning. Hoboken: John Wiley, 2005. ISBN 0-471-38859-9 .