| Course Unit Code | 450-4049/05 |
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| Number of ECTS Credits Allocated | 5 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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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 | Czech |
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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. |
| LAN177 | RNDr. Miroslav Langer, Ph.D. |
| KUB631 | Ing. Jan Kubíček, Ph.D. |
| Summary |
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| Subject deals with gathering of knowledge and applications of the artificial intelligence methods in the context of processing and modeling of the biomedical image data. Subject is composed from four main areas of the artificial intelligence. The first part of the subject deals with the fuzzy mathematics, fuzzy modeling, and design of the expert systems. The second part of the subject deals with the data classification with emphasis to an area of the neural network. Next area deals with optimization techniques with emphasis of an analysis of the genetic algorithms for solving of the complex mathematical problems. The last part of the subject focuses to hierarchical and non-hierarchical methods of the cluster analysis. |
| Learning Outcomes of the Course Unit |
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The subject represents the introduction to the principles of scientific field of artificial intelligence. The goal of subject is introduce students on analysis and design of artificial intelligence tolls in the field of biomedical engineering.
Students will be ready for practical use of basic artificial intelligence tools namely fuzzy expert systems, artificial neural networks and genetic algorithms in the field of BME.
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| Course Contents |
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Lectures
1. Principles and methods of artificial intelligence. Methods of computer representation of knowledge and language modeling.
2. Basics of fuzzy mathematics and fuzzy logic.
3. Fuzzy expert systems.
4. Fuzzy models.
5. Methods for verifying the design of fuzzy models
6. Basics of graph theory, definitions, graph search methods, problem, state space
7. Introduction to knowledge systems - definition, brief history, applications
8. Architecture of knowledge systems, knowledge base and fact base
9. Inference mechanism
10. Problems of the "select" function, quantitative and qualitative heuristics
11. Other modules of knowledge systems
12. Introduction to knowledge engineering, life cycle of knowledge system
Computer exercises
1. Introduction to mathematical modeling in MATLAB.
2.Methodology of fuzzy model design in MATLAB environment
3. Design of a fuzzy model focused on economics
4. Debugging fuzzy custom fuzzy models
5. Presentation of created fuzzy models
6. Shell Expert System Clips - introduction to working with the system
7. Variable, definition of facts and rules
8. Lists
9. Working with facts
10. Lists and multivalued variables
11. Auxiliary facts and priority of rules
12. Templates, subsetp command |
| 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.
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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
VESELÝ,A. Úvod do umělé inteligence. ČZU Praha, 2005. ISBN 80-213-1361-7
JURA, P. Základy fuzzy logiky pro řízení a modelování. Brno: Nakladatelství VUTIUM, 2003, ISBN 80-214-2261-0.
VONDRÁK, Ivo. Umělá inteligence a neuronové sítě. Ostrava: VŠB - Technická univerzita Ostrava, 1994. ISBN 80-7078-259-5.
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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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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 40 (40) | 21 |
| Návrh vlastního fuzzy modelu | Project | 20 | 5 |
| Vytvoření vlastního pravidlového systému v prostředí Clips | Project | 20 | 5 |
| Examination | Examination | 60 (60) | 30 |
| Realizace fuzzy modelu nebo pravidlového systému v prostředí Clips dle zadání | Other task type | 40 | 20 |
| Ústní zkouška | Oral examination | 20 | 5 |