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.
Applied Artificial Intelligence Methods
| Language of instruction | angličtina, čeština |
|---|---|
| Code | 450-4049 |
| Abbreviation | AUI |
| Course title | Applied Artificial Intelligence Methods |
| Coordinating department | Department of Cybernetics and Biomedical Engineering |
| Course coordinator | prof. Ing. Martin Černý, Ph.D. |
Anotace
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.
Upon successful completion of the course, the student will achieve the following learning outcomes:
Professional Knowledge
The student explains the principles of artificial intelligence and describes selected machine learning methods used in engineering practice. The student characterizes the principles of fuzzy logic, expert systems, neural networks, decision trees, cluster analysis methods, and optimization algorithms. The student compares the capabilities and limitations of individual methods, including generative artificial intelligence tools.
Professional Skills
The student applies selected artificial intelligence methods to solve technical problems. The student designs and implements a simple fuzzy expert system, neural network, or optimization algorithm. The student uses machine learning methods for data analysis, classification, prediction, or decision support. The student evaluates the achieved results and justifies the suitability of the selected solution with respect to the nature of the problem being solved.
General Competencies
The student independently solves technical problems using artificial intelligence methods. The student critically assesses input data, applied methods, and obtained results with respect to their limitations and practical applicability. The student presents the proposed solution and provides a professional justification of the chosen approach. The student collaborates in solving interdisciplinary tasks and evaluates the ethical, security, and societal aspects of artificial intelligence applications.
Upon successful completion of the course, the student will achieve the following learning outcomes:
Professional Knowledge
The student explains the principles of artificial intelligence and describes selected machine learning methods used in engineering practice. The student characterizes the principles of fuzzy logic, expert systems, neural networks, decision trees, cluster analysis methods, and optimization algorithms. The student compares the capabilities and limitations of individual methods, including generative artificial intelligence tools.
Professional Skills
The student applies selected artificial intelligence methods to solve technical problems. The student designs and implements a simple fuzzy expert system, neural network, or optimization algorithm. The student uses machine learning methods for data analysis, classification, prediction, or decision support. The student evaluates the achieved results and justifies the suitability of the selected solution with respect to the nature of the problem being solved.
General Competencies
The student independently solves technical problems using artificial intelligence methods. The student critically assesses input data, applied methods, and obtained results with respect to their limitations and practical applicability. The student presents the proposed solution and provides a professional justification of the chosen approach. The student collaborates in solving interdisciplinary tasks and evaluates the ethical, security, and societal aspects of artificial intelligence applications.
Povinná literatura
Doporučená literatura
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 .