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Lecturer(s)
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Jašek Roman, prof. Mgr. Ph.D., DBA
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Šenkeřík Roman, prof. Ing. Ph.D.
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Course content
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- the classical theory of informatics, - quantum information theory, - bio-informatics, - introduction to DNA computing and search algorithms of bio-informatics - deterministic chaos, - other unconventional approaches including eg fractal geometry, soft-computing, and fuzzy computation theory, - non-traditional approaches of the modern theory of informatics in many applications from various areas of human activity.
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Learning activities and teaching methods
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Individual work of students
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| prerequisite |
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| Knowledge |
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| Knowledge from areas: Theoretical informatics Mathematics |
| Knowledge from areas: Theoretical informatics Mathematics |
| learning outcomes |
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| The student can define and analyze the basic principles of classical computer science theory, including formal models of computation, automata and machines, grammars and languages, and understand modern approaches in computer science. |
| The student can define and analyze the basic principles of classical computer science theory, including formal models of computation, automata and machines, grammars and languages, and understand modern approaches in computer science. |
| The student has knowledge of computational complexity, explainability, interpretability, and principles of complex systems in the context of modern computer science. |
| The student has knowledge of computational complexity, explainability, interpretability, and principles of complex systems in the context of modern computer science. |
| The student understands uncertainty theory and its relationship with classical information theory, and knows calculus for working with fuzzy sets and multi-valued logic. |
| The student understands uncertainty theory and its relationship with classical information theory, and knows calculus for working with fuzzy sets and multi-valued logic. |
| The student has an overview of quantum information theory and basic principles of bio-informatics. |
| The student has an overview of quantum information theory and basic principles of bio-informatics. |
| The student understands various unconventional approaches in modern computer science theory, including fractal geometry and soft-computing. |
| The student understands various unconventional approaches in modern computer science theory, including fractal geometry and soft-computing. |
| Skills |
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| The student can apply modern theories of computer science, explainability and interpretability to solve interdisciplinary research problems. |
| The student can apply modern theories of computer science, explainability and interpretability to solve interdisciplinary research problems. |
| The student can use quantum and bio-informatics methods for specific research tasks. |
| The student can use quantum and bio-informatics methods for specific research tasks. |
| The student is able to analyze and solve complex computational problems using modern computer science principles. |
| The student is able to analyze and solve complex computational problems using modern computer science principles. |
| The student has skills in working with uncertainty and fuzzy logic to solve specific problems. |
| The student has skills in working with uncertainty and fuzzy logic to solve specific problems. |
| The student is able to use unconventional and innovative approaches in various applications of modern computer science theory. |
| The student is able to use unconventional and innovative approaches in various applications of modern computer science theory. |
| teaching methods |
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| Knowledge |
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| Individual work of students |
| Individual work of students |
| assessment methods |
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| Background research |
| Background research |
| Preparation of a presentation, giving a presentation |
| Preparation of a presentation, giving a presentation |
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Recommended literature
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Demel J. Grafy a jejich aplikace. Academia, 2002.
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Edgar, Gerald A. Measure, topology, and fractal geometry. New York : Springer, 1990. ISBN 387972722.
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Ilachinski, Andrew. Cellular automata: a discrete universe. 2001. ISBN 981-238-183-X.
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Kacprzyk J, Pedrycz, W. Springer handbook of computational intelligence. 2015.
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Klir, G. Uncertainty and information: foundations of generalized information theory. 2006. ISBN 0471748676.
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Koubková A., Pavelka J. Uvod do teoretické informatiky. Matfyzpress, 2003.
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Linz, Peter. An introduction to formal languages and automata. 4th ed. Sudbury, Mass. : Jones and Bartlett Publishers, 2006. ISBN 0-7637-3798-4.
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Manna Z. Matematická teorie programů. SNTL, 1981.
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Martin J.C. Introduction to Languages and the Theory of Computation. 2002. ISBN 0-072-32200-4.
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Mikhail J. Atallah. Algorithms and Theory of Computation Handbook. CRC-Press, 1998.
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Neil C. Jones, Pavel A. Pevzner. An Introduction to Bioinformatics Algorithms. The MIT Press, 2004.
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Nielsen, Michael A. Quantum computation and quantum information. 10th Anniversary ed. Cambridge : Cambridge University Press, 2010. ISBN 978-1-107-00217-3.
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Vaníček J., Papík M., Pregl R., Vaníček T. Teoretické základy informatiky. Alfa Publishing, 2006.
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Zygelman, B. A first introduction to quantum computing and information. New York, 2018. ISBN 978-3-319-91628-6.
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