470-4124/01 – Elements of Higher Mathematics II (EVM II)

Gurantor departmentDepartment of Applied MathematicsCredits6
Subject guarantorIng. Jan Kracík, Ph.D.Subject version guarantorIng. Jan Kracík, Ph.D.
Study levelundergraduate or graduateRequirementOptional
Year1Semestersummer
Study languageCzech
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
KRA0220 Ing. Jan Kracík, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Combined Credit and Examination 8+8

Subject aims expressed by acquired skills and competences

Students will understand basic concepts of probability theory, mathematical statistics and numerical methods.

Teaching methods

Lectures
Tutorials
Project work

Summary

The subject is intended for students of master's program Computational and applied mathematics without background in mathematical statistics and numerical methods.

Compulsory literature:

RAO, C. RADHAKRISHNA. Linear statistical inference and its applications. 2. ed., paperback ed. New York: Wiley, 2002. ISBN 0471218758. Quarteroni, R. Sacco, F. Saleri, Numerical Mathematics. Springer, 2007.

Recommended literature:

TEETOR, Paul. R cookbook. Sebastopol, CA: O'Reilly, 2011. ISBN 9780596809157 W.H. Press, B.P. Flannery, S.A. Teukolski, W.T. Vetterling, Numerical Recipes in C. Cambridge University Press, 19

Way of continuous check of knowledge in the course of semester

semestral project, test written and oral exam

E-learning

Další požadavky na studenta

No additional requirements are imposed on the student.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Probability theory: Probability space Random variable, random vector Selected probability distributions Limit theorems Mathematical statistics: Independent random samples Hypothesis tests Statistical estimation Analysis of variance Linear regression Numerical methods: Data fitting Numerical integration Iterative methods for solution of nonlinear equations Numerical solution to ordinary 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 30 (30) 10
                Projekt Project 10  5
                Zápočtová písemka Written test 20  5
        Examination Examination 70  35
Mandatory attendence parzicipation: participation at all exercises is obligatory, 2 apologies are accepted

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics P Czech Ostrava 1 Optional study plan
2019/2020 (N0541A170007) Computational and Applied Mathematics (S02) Computational Methods and HPC P Czech Ostrava 1 Optional study plan
2019/2020 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics K Czech Ostrava 1 Optional study plan
2019/2020 (N0541A170007) Computational and Applied Mathematics (S02) Computational Methods and HPC K Czech Ostrava 1 Optional study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner