638-3015/02 – Mathematical methods of computer data processing (MMPZD)

Gurantor departmentDepartment of Automation and Computing in IndustryCredits5
Subject guarantordoc. Ing. Jiří David, Ph.D.Subject version guarantordoc. Ing. Jiří David, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Study languageCzech
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFMTIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
DAV47 doc. Ing. Jiří David, Ph.D.
FRI05 doc. Ing. Robert Frischer, Ph.D.
GRY0035 Ing. Ondřej Grycz, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Part-time Credit and Examination 16+0

Subject aims expressed by acquired skills and competences

The student will be able to interpret the concepts and principles of mathematical methods and numerical calculations using the Matlab software system in data processing in the field of engineering tasks. He will have an overview of the methods and tools of computerized data processing in the design of materials and means for the automotive industry. He will be able to apply selected methods and tools of computer data processing in solving practically oriented problems of practice using the Matlab environment. It will be able to implement principles and methods in the processes and activities of industrial organizations.

Teaching methods

Project work


347/5000 Objective of the course in terms of learning outcomes and competences The course is focused on acquiring a basic set of knowledge about the principles of mathematical methods of computer data processing in the solution of engineering calculations. Emphasis is placed on gaining practical experience with the use of the discussed methods, estimation of the errors of the result and demonstration of their properties in solving engineering problems using the Matlab program.

Compulsory literature:

MOLER, C. B. Numerical Computing with Matlab. Philadelphia: Society for Industrial and Applied Mathematics, 2004. ISBN: 978-0898715606 CHAPRA, S. C. Applied Numerical Methods W/MATLAB: for Engineers & Scientists. Columbus: McGraw-Hill Education; 2011. ISBN: 978-0073401102 DASGUPTA, S. , CH. H. PAPADIMITRIOU a U. VAZIRANI. Algorithms. Columbus: McGraw-Hill Education; 2006. ISBN: 978-0073523408 ALFIO, Q. Scientific Computing with MATLAB and Octave. Berlin: Springer, 2014. ISBN: 978-3642124297. LEADER, J. J. Numerical Analysis and Scientific Computation. London: Pearson. 2004. ISBN: 978-0201734997.

Recommended literature:

KIUSALAAS, J. Numerical Methods in Engineering with MATLAB. Cambridge: Cambridge University Press, 2015. ISBN: 9781107120570. CHARTIER, T. P. a A. GREENBAUM. Numerical Methods: Design, Analysis, and Computer Implementation of Algorithms. Princeton :Princeton University Press. 2012. ISBN: 978-0691151229. WITTEN, I. H., E. FRANK a M. A. HALL. Data Mining: Practical Machine Learning Tools and Techniques: Practical Machine Learning Tools and Techniques. Elsevier, 2011. ISBN 0080890369. TAN, P. N. Introduction To Data Mining. Pearson Education, 2007. ISBN 8131714721.

Way of continuous check of knowledge in the course of semester

Písemný test a ústní zkoušení.


Other requirements

Active participation in seminars, successful passing the credit test - full-time study form. Elaboration of the seminar work on the given topic - combined form of study. Practical solutions to the task of engineering character of numerical methods and using software system Matlab.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

1. Engineering tables and calculations 2. Problems errors, compliance and stability calculations. 3. Methods for solving nonlinear equations. 4. Direct methods for solving linear equations. Eigenvalues and vectors, their numerical calculation. 5. Iterative methods for solving linear equations. 6. Approximation of functions, least squares method 7. The interpolation function 8. Increasing the accuracy of calculations extrapolating 9. Complex tasks using analytical tools 10. Numerical calculation of integrals and derivatives 11. Linear Programming 12. Nonlinear Programming 13. The step methods for solving initial value problems for ordinary differential equations. Multistep methods. 14. Ordinary Differential Equations - initial value problems and boundary value problems. 15. The system of differential equations

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 35  20
        Examination Examination 65  16
Mandatory attendence parzicipation: Testing

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (N0715A270004) Materials and technologies for the automotive industry P Czech Ostrava 1 Compulsory study plan
2020/2021 (N0715A270004) Materials and technologies for the automotive industry P Czech Ostrava 1 Compulsory study plan
2019/2020 (N0715A270004) Materials and technologies for the automotive industry P Czech Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner