# FI:MV013 Statistics for CS - Course Information

## MV013 Statistics for Computer Science

**Faculty of Informatics**

Spring 2024

**Extent and Intensity**- 2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).

Taught in person. **Teacher(s)**- RNDr. Radim Navrátil, Ph.D. (lecturer)

Reza Dastranj, MSc (seminar tutor)

Mgr. Pavel Morcinek (seminar tutor) **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 8:00–9:50 D3
- Timetable of Seminar Groups:

*P. Morcinek*

MV013/02: Mon 18:00–19:50 A215,*P. Morcinek*

MV013/03: Wed 8:00–9:50 A320,*R. Dastranj*

MV013/04: Wed 10:00–11:50 A320,*R. Dastranj*

MV013/05: Fri 8:00–9:50 A320,*R. Dastranj*

MV013/06: Fri 10:00–11:50 A320,*R. Dastranj* **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 and tests during the semester (40 points), final written exam (60 points). At least 50 % of averall points is needed to pass.
**Language of instruction**- English
**Further comments (probably available only in Czech)**- Study Materials

The course is taught annually. **Teacher's information**- Capacity of the course is limited. Registration is required.

- Enrolment Statistics (recent)

- Permalink: https://is.muni.cz/course/fi/spring2024/MV013