638-3001/01 – Mathematical Tools of Informatics (MPI)

Gurantor departmentDepartment of Automation and Computing in IndustryCredits7
Subject guarantordoc. Ing. Jiří David, Ph.D.Subject version guarantordoc. Ing. Jiří David, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Year2Semesterwinter
Study languageCzech
Year of introduction2014/2015Year of cancellation
Intended for the facultiesFMTIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
DAV47 doc. Ing. Jiří David, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 4+3
Combined Credit and Examination 18+0

Subject aims expressed by acquired skills and competences

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.

Teaching methods

Lectures
Tutorials

Summary

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.

Compulsory literature:

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 literature:

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.

Way of continuous check of knowledge in the course of semester

E-learning

Další požadavky na studenta

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

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

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.

Conditions for subject completion

Combined form (validity from: 2014/2015 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Exercises evaluation and Examination Credit and Examination 100 (100) 51
        Exercises evaluation Credit 35  25
        Examination Examination 65  26
Mandatory attendence parzicipation:

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Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (N3922) Economics and Management of Industrial Systems (3902T042) Automation and Computing in Industrial Technologies K Czech Ostrava 2 Compulsory study plan
2019/2020 (N3922) Economics and Management of Industrial Systems (3902T042) Automation and Computing in Industrial Technologies P Czech Ostrava 2 Compulsory study plan
2018/2019 (N3922) Economics and Management of Industrial Systems (3902T042) Automation and Computing in Industrial Technologies P Czech Ostrava 2 Compulsory study plan
2018/2019 (N3922) Economics and Management of Industrial Systems (3902T042) Automation and Computing in Industrial Technologies K Czech Ostrava 2 Compulsory study plan
2017/2018 (N3922) Economics and Management of Industrial Systems (3902T042) Automation and Computing in Industrial Technologies P Czech Ostrava 2 Compulsory study plan
2017/2018 (N3922) Economics and Management of Industrial Systems (3902T042) Automation and Computing in Industrial Technologies K Czech Ostrava 2 Compulsory study plan
2016/2017 (N3922) Economics and Management of Industrial Systems (3902T042) Automation and Computing in Industrial Technologies P Czech Ostrava 1 Compulsory study plan
2016/2017 (N3922) Economics and Management of Industrial Systems (3902T042) Automation and Computing in Industrial Technologies K Czech Ostrava 1 Compulsory study plan
2015/2016 (N3922) Economics and Management of Industrial Systems (3902T042) Automation and Computing in Industrial Technologies P Czech Ostrava 1 Compulsory study plan
2015/2016 (N3922) Economics and Management of Industrial Systems (3902T042) Automation and Computing in Industrial Technologies K Czech Ostrava 1 Compulsory study plan
2014/2015 (N3922) Economics and Management of Industrial Systems (3902T042) Automation and Computing in Industrial Technologies P Czech Ostrava 1 Compulsory study plan
2014/2015 (N3922) Economics and Management of Industrial Systems (3902T042) Automation and Computing in Industrial Technologies K Czech Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner