450-4049/04 – Applied Artificial Intelligence Methods (AUI)

Gurantor departmentDepartment of Cybernetics and Biomedical EngineeringCredits4
Subject guarantorprof. Ing. Martin Černý, Ph.D.Subject version guarantorprof. Ing. Martin Černý, Ph.D.
Study levelundergraduate or graduate
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
CER275 prof. Ing. Martin Černý, Ph.D.
KUB631 Ing. Jan Kubíček, Ph.D.
LAN177 RNDr. Miroslav Langer, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Graded credit 2+2
Part-time Graded credit 0+16

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

Way of continuous check of knowledge in the course of semester

Ongoing monitoring of studies: Continuous monitoring is carried out on the basis of the student's participation in laboratory exercises. Conditions for awarding classified credit: The student may achieve 60 points for the completion of the tasks in the practical computer exercises. The minimum number of points to be awarded is 20. To pass the course, the student must pass a written test focused on theoretical knowledge with a minimum score of 20 out of 40 possible 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. 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

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester, validity until: 2024/2025 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Graded credit Graded credit 100 (100) 51 3
        Protocols from laboratory exercises Laboratory work 40  20
        Written test Written test 60  30 2
Mandatory attendence participation: protocols from laboratory exercises and a written test maximální dovolená neúčast na cvičeních je 20 %/ the maximum leave of absence is 20 %

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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

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Part-time form (validity from: 2019/2020 Winter semester, validity until: 2024/2025 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Graded credit Graded credit 100 (100) 51 3
        Project Semestral project 40  20
        Written test Written test 60  30 2
Mandatory attendence participation: semestral project and a written test 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 yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2025/2026 (N0988A060002) Biomedical Engineering MZD P English Ostrava 1 Compulsory study plan
2024/2025 (N0716A060002) Automotive Electronic Systems P English Ostrava 2 Compulsory study plan
2024/2025 (N0988A060002) Biomedical Engineering MZD P English Ostrava 2 Compulsory study plan
2023/2024 (N0714A060007) Applied Electronics P English Ostrava 2 Optional study plan
2023/2024 (N0716A060002) Automotive Electronic Systems P English Ostrava 2 Optional study plan
2023/2024 (N0988A060002) Biomedical Engineering MZD P English Ostrava 2 Compulsory study plan
2022/2023 (N0988A060002) Biomedical Engineering MZD K English Ostrava 2 Compulsory study plan
2022/2023 (N0988A060002) Biomedical Engineering MZD P English Ostrava 2 Compulsory study plan
2022/2023 (N0714A060007) Applied Electronics P English Ostrava 2 Optional study plan
2022/2023 (N0716A060002) Automotive Electronic Systems P English Ostrava 2 Optional study plan
2021/2022 (N0988A060002) Biomedical Engineering MZD P English Ostrava 2 Compulsory study plan
2021/2022 (N0988A060002) Biomedical Engineering MZD K English Ostrava 2 Compulsory study plan
2021/2022 (N0716A060002) Automotive Electronic Systems P English Ostrava 2 Optional study plan
2021/2022 (N0714A060007) Applied Electronics P English Ostrava 2 Optional study plan
2020/2021 (N0988A060002) Biomedical Engineering MZD P English Ostrava 2 Compulsory study plan
2020/2021 (N0988A060002) Biomedical Engineering MZD K English Ostrava 2 Compulsory study plan
2020/2021 (N0714A060007) Applied Electronics P English Ostrava 2 Optional study plan
2020/2021 (N0716A060002) Automotive Electronic Systems P English Ostrava 2 Optional study plan
2019/2020 (N0988A060002) Biomedical Engineering MZD P English Ostrava 2 Compulsory study plan
2019/2020 (N0988A060002) Biomedical Engineering MZD K English Ostrava 2 Compulsory study plan
2019/2020 (N0714A060007) Applied Electronics P English Ostrava 2 Optional study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner
ECTS - mgr. 2025/2026 Full-time English Optional 401 - Study Office stu. block
ECTS - mgr. 2024/2025 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2023/2024 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2022/2023 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2021/2022 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2020/2021 Full-time English Optional 401 - Study Office stu. block

Assessment of instruction



2020/2021 Summer