Gurantor department | Department of Automation and Computing in Industry | Credits | 5 |

Subject guarantor | doc. Ing. Jiří David, Ph.D. | Subject version guarantor | doc. Ing. Jiří David, Ph.D. |

Study level | undergraduate or graduate | Requirement | Compulsory |

Year | 1 | Semester | summer |

Study language | Czech | ||

Year of introduction | 2019/2020 | Year of cancellation | |

Intended for the faculties | FMT | Intended for study types | Follow-up Master |

Instruction secured by | |||
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Login | Name | Tuitor | Teacher giving lectures |

DAV47 | doc. Ing. Jiří David, Ph.D. |

Extent of instruction for forms of study | ||
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Form of study | Way of compl. | Extent |

Full-time | Credit and Examination | 3+2 |

Part-time | Credit and Examination | 16+0 |

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.

Lectures

Tutorials

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.

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.

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.

Písemný test a ústní zkoušení.

Getting to know with practical solutions to optimization problems using of genetic algorithms.

Subject has no prerequisities.

Subject has no co-requisities.

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.

Task name | Type of task | Max. number of points
(act. for subtasks) | Min. number of points | Max. počet pokusů |
---|---|---|---|---|

Credit and Examination | Credit and Examination | 100 (100) | 51 | |

Credit | Credit | 35 | 25 | |

Examination | Examination | 65 | 26 | 3 |

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Conditions for subject completion and attendance at the exercises within ISP:

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Academic year | Programme | Branch/spec. | Spec. | Zaměření | Form | Study language | Tut. centre | Year | W | S | Type of duty | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

2021/2022 | (N0413A270002) Quality Management and Control of Industrial Systems | (S03) Intelligent Control Systems in Industry | IŘ | P | Czech | Ostrava | 1 | Compulsory | study plan | |||

2021/2022 | (N0413A270002) Quality Management and Control of Industrial Systems | (S03) Intelligent Control Systems in Industry | IŘ | K | Czech | Ostrava | 1 | Compulsory | study plan | |||

2020/2021 | (N0413A270002) Quality Management and Control of Industrial Systems | (S03) Intelligent Control Systems in Industry | IŘ | K | Czech | Ostrava | 1 | Compulsory | study plan | |||

2020/2021 | (N0413A270002) Quality Management and Control of Industrial Systems | (S03) Intelligent Control Systems in Industry | IŘ | P | Czech | Ostrava | 1 | Compulsory | study plan | |||

2019/2020 | (N0413A270002) Quality Management and Control of Industrial Systems | (S03) Intelligent Control Systems in Industry | IŘ | P | Czech | Ostrava | 1 | Compulsory | study plan | |||

2019/2020 | (N0413A270002) Quality Management and Control of Industrial Systems | (S03) Intelligent Control Systems in Industry | IŘ | K | Czech | Ostrava | 1 | Compulsory | study plan |

Block name | Academic year | Form of study | Study language | Year | W | S | Type of block | Block owner |
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2019/2020 Summer |