SYLLABUS

University: Technical University of Košice

Faculty: Faculty of Electrical Engineering and Informatics

Department: Department of Mathematics and Theoretical Informatics

Course Number: 2619661

Course Name: Numerical Mathematics, Probability Theory and Mathematical Statistics

Type, scope and method of learning activities:
Course Type: Lecture, Numerical exercises
Recommended scope of the course content (in hours):
Full-time study (hours per week): 3,2
Part-time study (hours per semester): ST 39,26
Study Method: Attendance

Number of credits: 6

Recommended semester of study: ST

Recommended semester

Study programme

Study grade

Study Method

1.rok ST

Electrical Power Engineering (EE_Ing_D_sk)
Electrical Power Engineering (EE_Ing_D_en)
Electrical Power Engineering (EE_Ing_D_KM)

Master
Master
Master

Attendance
Attendance
Combined

2.rok ST

Physical Engineering of Advanced Materials (FIPM_Bc_D_sk)
Automotive Electronics (AE_Bc_D_sk)
Intelligent Systems (IntS_Bc_D_sk)
Intelligent Systems (IntS_Bc_D_en)
Computer networks (PS_Bc_D_sk)
Computer Modelling (PM_Bc_D_sk)

Bachelor
Bachelor
Bachelor
Bachelor
Bachelor
Bachelor

Attendance
Attendance
Attendance
Attendance
Attendance
Attendance

Level of study: Bachelor

Prerequisites:

Course completion requirements:
Assessment and completion of the course: Credit test and examination
Continuous assessment: Student passes the continuous assessment and receives credits when he or she meets the requirement to obtain at least 1u out of 1u21% out of 40%.
written test
Final assessment: Student passes the final assessment and passes the examination when he or she meets the requirement to obtain at least 31% out of 60%.
oral form exam
Overall assessment: Overall assessment is the sum of the assessments obtained by students in the assessment period. The overall result is determined in accordance with the internal regulations of the Technical University in Košice. (Study Regulations, the internal regulation principles of doctoral studies)

Learning outcomes:
To provide basic knowledge of numerical mathematics and its use in solving practical problems. Develop logical thinking by solving problems from probability. Introduce students to the basics of mathematical statistics.

Brief course content:
1. Separation of roots of a nonlinear equation. Numerical solution of nonlinear equations by a bisection method.
2. Numerical solution of nonlinear equations by Newton's method and iteration method.
3. Solving a system of nonlinear equations by Newton's method.
4. Approximation of functions by interpolation and least-squares method.
5. Numerical calculation of a definite integral by trapezoidal method and Simpson's method.
6. Random events and probability.
7. Conditional probability, independent events, Total probability law, and Bayes' theorem.
8. Random variable and its characteristics. Expective value and variance.
9. Some probability distributions of a discrete random variable - binomial, hypergeometric, and Poisson distributions.
10. Probability distributions of a continuous random variable - exponential and Gaussian (normal) distribution.
11. Mathematical statistics - basic concepts and characteristics of random selection.
12. Confidence intervals for random characteristics.
13. Testing statistical hypotheses.

Recommended Reference Sources:
1. Buchanan, J. L. - Turner, P. R.: Numerical Methods and Analysis. McGraw-Hill, Inc. 1992.
2. Hanke, J. E. - Reitsch, A. G.: Understanding Business Statistics. Irwin, 1991.

Recommended optional program components:

Languages required for the course completion: Slovak

Notes: To successfully complete the course, it is necessary to obtain a credit and successfully pass the exam. This includes the student's participation in educational activities of direct teaching, lectures, exercises, as well as independent study and independent creative activity of the student in processing the semester assignment / assignments, project on a specified topic,
to a specified extent, in a specified design of a total of 150 hours intensity of the student's work per semester.

Course assessment:
Total number of students assessed: 3461

11%

20%

29%

32%

Teacher:
doc. RNDr. Marián Klešč, PhD.
RNDr. Zuzana Gibová, PhD.
RNDr. Emília Draženská, PhD.
RNDr. Štefan Berežný, PhD.
RNDr. Mária Timková, PhD.
RNDr. Mária Hutníková, PhD.
RNDr. Michal Staš, PhD.
doc. RNDr. Helena Myšková, PhD.
RNDr. Juraj Valiska, PhD.

Last modified: 31.08.2022

Approved by: person(s) responsible for the study program