| Course Unit Code | 638-3001/03 |
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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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| DAV47 | doc. Ing. Jiří David, Ph.D. |
| Summary |
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| Subjekt put mind to the questions solution numerical problems. Students do one's homework the to determine a mine of errors and errors type at the numerical computing, to principles the modern optimization methods and determine a procedure solution with utilization the Genetic Algorithms and the Evolutional Algorithms and get an overview of the basic principles of the datamining metods and of the basic acquirements at solution numerical problems with utilization the Matlab and with utilization the Matlab Toolbox Genetic Algorithm. |
| Learning Outcomes of the Course Unit |
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Student will be able to determine a mine of errors and errors type at the numerical computing.
Student will be able to principles the modern optimization methods and determine a procedure solution with utilization the Genetic Algorithms and the Evolutional Algorithms.
Student will get an overview of the basic principles of the datamining metods and of the basic acquirements at solution numerical probléme with utilization the Matlab and with utilization the Matlab Toolbox Genetic Algorithm. |
| Course Contents |
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1. Errors, sources and types errors. Rounding error. Errors of method.
2. Incomplete numbers and number representation in computer. Correctitude, conditionality and stability numerical problems.
3. Optimalization problems. Classification of optimization methods.
4. Principles of basic of optimization methods. Evolutional methods.
5. Principle of genetic algorithm.
6. Fitness value. Code of strings.
7. Termination of genetic algorithm. Stagnation of genetic algorithm.
8. Selection of strings. Principles of particular method of selection.
9. Crossing. Mutation. Types of mutation
10. Variants of genetic algoritm.
11. Principle of evolutional strategy. Principle of differential evolution. Principle of SOMA. Principle of UIS.
12. Data warehouse.
13. Data mining. Data mining problems.
14. Principle of methodology CRISP- DM.
15. Data miningu methods . Principle of decision - making trees.
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| Recommended or Required Reading |
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| Required Reading: |
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WITTEN I. H., E. FRANK and M.A. HALL. Data Mining: Practical Machine Learning Tools and Techniques: Practical Machine Learning Tools and Techniques. Elsevier, 2011. ISBN 0080890369.
KIM, K. et. al. Genetic Algorithms.: Concepts and Designs. London: Springer, 1999. ISBN 1852330724.
TAN P. N.: Introduction To Data Mining. Pearson Education, 2007. ISBN 8131714721.
BACK, T. Evolutionary Algorithms in Theory and Practice : Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford: Oxford University Press, 1995. ISBN 0195356705.
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DAVID, J. Matematické prostředky informatiky. VŠB-TU Ostrava, Ostrava, 2013.
BERKA, P. Dobývání znalostí z databází. Praha : Academia, 2003, ISBN 80-200-1062-9
ZELINKA, I. Umělá inteligence v problémech globální optimalizace. Praha: BEN - technická literatura, 2002. ISBN: 80-7300-069-5.
WITTEN I. H., E. FRANK and M.A. HALL. Data Mining: Practical Machine Learning Tools and Techniques: Practical Machine Learning Tools and Techniques. Elsevier, 2011. ISBN 0080890369.
KIM, K. et. al. Genetic Algorithms.: Concepts and Designs. London: Springer, 1999. ISBN 1852330724.
TAN P. N.: Introduction To Data Mining. Pearson Education, 2007. ISBN 8131714721.
BACK, T. Evolutionary Algorithms in Theory and Practice : Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford: Oxford University Press, 1995. ISBN 0195356705. |
| Recommended Reading: |
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KIUSALAAS, J. Numerical Methods in Engineering with MATLAB. Cambridge: Cambridge University Press, 2015. ISBN: 9781107120570.
CHARTIER, T. P. and A. GREENBAUM. Numerical Methods: Design, Analysis, and Computer Implementation of Algorithms. Princeton :Princeton University Press. 2012. ISBN: 978-0691151229.
WALTER J. G. and T. L. VINCENT Modern control systems analysis and design. New York : John Wiley & Sons, Inc., 1993. ISBN 0-471-81193-9.
YAO, X. Evolutionary Computation: Theory and Applications. World Scientific, 1999. ISBN 9810223064.
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OPLATKOVÁ, Z. et. al. Evoluční výpočetní techniky - principy a aplikace. BEN-Technická literatura, 2008. ISBN: 80-7300-218-3.
KIUSALAAS, J. Numerical Methods in Engineering with MATLAB. Cambridge: Cambridge University Press, 2015. ISBN: 9781107120570.
CHARTIER, T. P. and A. GREENBAUM. Numerical Methods: Design, Analysis, and Computer Implementation of Algorithms. Princeton :Princeton University Press. 2012. ISBN: 978-0691151229.
WALTER J. G. and T. L. VINCENT Modern control systems analysis and design. New York : John Wiley & Sons, Inc., 1993. ISBN 0-471-81193-9.
YAO, X. Evolutionary Computation: Theory and Applications. World Scientific, 1999. ISBN 9810223064. |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| 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 | 35 | 25 |
| Examination | Examination | 65 | 26 |