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Terminated in academic year 2021/2022

Mathematical methods of computer data processing

Type of study Follow-up Master
Language of instruction Czech
Code 638-3015/02
Abbreviation MMPZD
Course title Mathematical methods of computer data processing
Credits 5
Coordinating department Department of Automation and Computing in Industry
Course coordinator doc. Ing. Jiří David, Ph.D.

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

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 .

Advised 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 .


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