# FI:MV013 Statistics for CS - Course Information

## MV013 Statistics for Computer Science

**Faculty of Informatics**

Spring 2022

**Extent and Intensity**- 2/2/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
**Teacher(s)**- RNDr. Radim Navrátil, Ph.D. (lecturer)

RNDr. Veronika Eclerová, Ph.D. (seminar tutor)

Mgr. Stanislav Zámečník (seminar tutor)

Mgr. et Mgr. Filip Zlámal, Ph.D. (seminar tutor)

Mgr. Markéta Makarová (assistant)

Mgr. Pavel Morcinek (assistant)

Mgr. Ondřej Pokora, Ph.D. (assistant)

doc. RNDr. Vojtěch Řehák, Ph.D. (assistant) **Guaranteed by**- prof. RNDr. Jan Slovák, DrSc.

Department of Computer Science – Faculty of Informatics

Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science **Timetable**- Tue 15. 2. to Tue 10. 5. Tue 8:00–9:50 D2
- Timetable of Seminar Groups:

*V. Eclerová*

MV013/02: Tue 15. 2. to Tue 10. 5. Tue 18:00–19:50 B204,*V. Eclerová*

MV013/03: Mon 14. 2. to Mon 9. 5. Mon 12:00–13:50 B204,*S. Zámečník*

MV013/04: Mon 14. 2. to Mon 9. 5. Mon 8:00–9:50 B204,*S. Zámečník*

MV013/05: Wed 16. 2. to Wed 11. 5. Wed 8:00–9:50 A215,*F. Zlámal*

MV013/06: Wed 16. 2. to Wed 11. 5. Wed 10:00–11:50 A215,*F. Zlámal* **Prerequisites**- Basic knowledge of mathematical analysis: functions, limits of sequences and functions, derivatives and integrals of real and multidimensional functions.

Basic knowledge of linear algebra: matrices and determinants, eigenvalues and eigenvectors.

Basic knowledge of probability theory: probability, random variables and vectors, limit theorems. **Course Enrolment Limitations**- The course is also offered to the students of the fields other than those the course is directly associated with.
**fields of study / plans the course is directly associated with**- there are 37 fields of study the course is directly associated with, display
**Course objectives**- The main goal of the course is to become familiar with some basic principles of statistics, with writing about numbers (presenting data using basic characteristics and statistical graphics), some basic principles of likelihood and statistical inference; to understand basic probabilistic and statistical models; to understand and explain basic principles of parametric statistical inference for continuous and categorical data; to implement these techniques to R language; to be able to apply them to real data.
**Learning outcomes**- Student will be able:

- to understand principles of likelihood and statistical inference for continuous and discrete data;

- to select suitable probabilistic and statistical model for continous and discrete data;

- to use suitable basic characteristics and statistical graphics for continous and discrete data;

- to build up and explain suitable statistical test for continuous and discrete data;

- to apply statistical inference on real continuous and discrete data;

- to apply simple linear regression model including ANOVA on real continuous data;

- to implement statistical methods of continuous and discrete data to R. **Syllabus**- What is statistics? Motivation and examples.
- Exploratory data analysis
- Revision of probability theory
- Parametric models - methods for parameter estimation
- Confidence intervals and hypothesis testing
- Testing hypotheses about one-sample
- Testing hypotheses about two-samples
- ANOVA
- Testing for independence
- Nonparametric tests
- Linear regression models

**Literature****Teaching methods**- Lectures, practical exercise classes with computers.
**Assessment methods**- Homeworks (assignments, 40 points), final written test (60 points). At least 50 % of averall points is needed to pass.
**Language of instruction**- English
**Further Comments**- Study Materials

The course is taught annually.

- Enrolment Statistics (Spring 2022, recent)
- Permalink: https://is.muni.cz/course/fi/spring2022/MV013