450-4049/01 – Applied Artificial Intelligence Methods (AUI)

Gurantor department	Department of Cybernetics and Biomedical Engineering	Credits	4
Subject guarantor	prof. Ing. Martin Černý, Ph.D.	Subject version guarantor	prof. Ing. Martin Černý, Ph.D.
Study level	undergraduate or graduate	Requirement	Optional
Year		Semester	winter
		Study language	Czech
Year of introduction	2010/2011	Year of cancellation
Intended for the faculties	FEI	Intended for study types	Follow-up Master

Instruction secured by
Login	Name	Tuitor	Teacher giving lectures
CER275	prof. Ing. Martin Černý, Ph.D.
KUB631	Ing. Jan Kubíček, Ph.D.

Extent of instruction for forms of study
Form of study	Way of compl.	Extent
Full-time	Graded credit	2+2
Part-time	Graded credit	2+12

Subject aims expressed by acquired skills and competences

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.

Teaching methods

Lectures
Tutorials
Experimental work in labs

Summary

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.

Compulsory literature:

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.

Recommended 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.

Additional study materials

Study supports in the EduDocs system

Way of continuous check of knowledge in the course of semester

Verification of study: The current activity of student is done through his laboratories activities. Conditions for graded credit: Student can gain up to 40 points from the laboratory excercices. To pass the laboratory part of the course student has to gain at least 21 points To pass the course student has to pass both of the laboratory part of the course and the final written proof 60, min 30 points.

E-learning

Other requirements

There are not defined other requirements for students

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Lectures: 1. Introduction on artificial intelligence scientific field 2. Principles of artificial intelligence, importace of knowledge in problem solving tasks 3. Methods of computer knowledge reprezentation 4. Mathematic and linguistc modelling 5. Vagueness formalization of knowledge in linguistic models 6. Principles of fuzzy set and fuzy logic theory 7. Fuzzy models of Mandami type 8. Fuzzy models of Takagi-Sugeno type 9. Diagnostoc expert systems 10. Fuzzy controllers 11. Topology and functions of multilayer artificial neural networks 12. Neural networks application in biomedical engineering 13. Genetic algorithms - versatil optimization methods 14. Genetic algorithms application in adaptation procedures Laboratories: 1. Fuzzy controller using microcomputer and PLC Computer labs: 1. Computer system MATLAB 2. Fuzzy ToolBox in MATLAB 3. Fuzzy sets and vagueness objects reprezentation in MATLAB 4. Fuzzy conditonal rules formalization in MATLAB 5. Fuzzy modelling of Mandami type in MATLAB 6. Fuzzy modelling of Takagi-Sugeno type in MATLAB 7. Diagnostic fuzzy expert systems 8. Fuzzy controllers in MATLAB 9. Pletysmogram evaluation fuzzy expert module 10. EEG evaluation fuzzy expert module 11. Neural network synthesi in Neural ToolBoxu of MATLABu 12. Neural network application in biomedical engineering 13. Genetic algorithm synthesis in MATLAB

Conditions for subject completion

Part-time form (validity from: 2014/2015 Winter semester)

Task name	Type of task	Max. number of points (act. for subtasks)	Min. number of points	Max. počet pokusů
Graded credit	Graded credit	100 (100)	51	3
Laboratorní práce	Laboratory work	40	21
Písemná práce	Written test	60	30	2

Mandatory attendence participation: maximální dovolená neúčast na cvičeních je 20 %/ the maximum leave of absence is 20 %

Show history

Conditions for subject completion and attendance at the exercises within ISP: Splnění všech povinných úkolů v individuálně dohodnutých termínech./Completion of all mandatory tasks within individually agreed deadlines

Show history

Occurrence in study plans

Academic year	Programme	Branch/spec.	Form	Study language	Tut. centre	Year	Type of duty
2022/2023	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava		Optional	study plan
2021/2022	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava		Optional	study plan
2021/2022	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava		Optional	study plan
2020/2021	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava		Optional	study plan
2020/2021	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava		Optional	study plan
2019/2020	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava		Optional	study plan
2019/2020	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava		Optional	study plan
2018/2019	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava		Optional	study plan
2018/2019	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava		Optional	study plan
2017/2018	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava		Optional	study plan
2017/2018	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava		Optional	study plan
2016/2017	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava		Optional	study plan
2016/2017	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava		Optional	study plan
2015/2016	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava		Optional	study plan
2015/2016	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava		Optional	study plan
2014/2015	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava		Optional	study plan
2014/2015	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava		Optional	study plan
2013/2014	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava		Optional	study plan
2013/2014	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava		Optional	study plan
2012/2013	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava		Optional	study plan
2012/2013	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava		Optional	study plan
2011/2012	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava		Optional	study plan
2011/2012	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava		Optional	study plan
2010/2011	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava	1	Optional	study plan
2010/2011	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	P	Czech	Ostrava	2	Optional	study plan
2010/2011	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava	1	Optional	study plan
2010/2011	(N2649) Electrical Engineering	(3901T009) Biomedical Engineering	K	Czech	Ostrava	2	Optional	study plan

Occurrence in special blocks

Block name	Academic year	Form of study	Study language	Year	W	S	Type of block	Block owner

Assessment of instruction