# FI:MV013 Statistics for CS - Course Information

## MV013 Statistics for Computer Science

**Faculty of Informatics**

Spring 2020

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

Mgr. Jan Böhm (seminar tutor)

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

Mgr. Markéta Janošová (seminar tutor)

Mgr. Stanislav Zámečník (seminar tutor) **Guaranteed by**- doc. PaedDr. RNDr. Stanislav Katina, Ph.D.

Department of Computer Science – Faculty of Informatics

Supplier department: Faculty of Science **Timetable**- Mon 17. 2. to Fri 15. 5. Tue 8:00–9:50 D3
- Timetable of Seminar Groups:

*S. Zámečník*

MV013/02: Mon 17. 2. to Fri 15. 5. Thu 14:00–15:50 A215,*S. Zámečník*

MV013/03: Mon 17. 2. to Fri 15. 5. Tue 16:00–17:50 A215,*S. Zámečník*

MV013/04: Mon 17. 2. to Fri 15. 5. Mon 10:00–11:50 A320,*M. Janošová*

MV013/05: Mon 17. 2. to Fri 15. 5. Mon 12:00–13:50 A320,*M. Janošová*

MV013/06: Mon 17. 2. to Fri 15. 5. Wed 16:00–17:50 B130,*J. Böhm*

MV013/07: Mon 17. 2. to Fri 15. 5. Thu 16:00–17:50 B204,*V. Eclerová* **Prerequisites**- The knowledge of basic calculus, linear algebra and theory of probability.
**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**- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (eng.) (programme FI, N-IN)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, B-AP)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-GE)
- Informatics with another discipline (programme FI, B-GK)
- Informatics with another discipline (programme FI, B-CH)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Mathematical Informatics (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, B-IN)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)

**Course objectives**- The main goal of the course is to become familiar with some basic principles of data science and 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 base on Wald principle, likelihood and score principle connecting the statistical theory with implementation in R, geometry, and statistical graphics; 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**- Why computer scientists should study statistics?
- Computer science related problems with analysed data
- Why the thought study based on data is useful?
- Data types
- Sampling
- Parametric probabilistic and statistical models
- Likelihood principle and parameter estimation using numerical methods
- Descriptive statistics (tables, listings, figures)
- From description to statistical inference
- Hypothesis testing and parameters of a model
- Goodness-of-fit tests
- Testing hypotheses about one-sample
- Testing hypotheses about two-samples
- Testing hypotheses about more than two sample problems including ANOVA
- Simple linear regression model
- Interpretation of statistical findings

**Literature**- CASELLA, George and Roger L. BERGER.
*Statistical inference*. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 0534243126. info

- CASELLA, George and Roger L. BERGER.
**Teaching methods**- Lectures, practicals.
**Assessment methods**- Homework (project), electronic test.
**Language of instruction**- English
**Further comments (probably available only in Czech)**- Study Materials

The course is taught annually. **Teacher's information**- Lectures
Even though lectures are not mandatory, you are highly encouraged to attend them. Practicals are an addition to lectures, not replacement. You are expected to keep up with lectures, otherwise you will be lost during practicals.

Slides

Slides will be published in "Study Materials" sequentially during semester after the particular topic is finished.

Practicals

Practicals are mandatory. You are allowed max. 2 unexcused absences. In case you have more unexcused absences, you get mark X and cannot sit the exam. Absences are considered excused if they are excused in accordance with MU Study and Examination Regulations, section 9: (7) A student is obliged to provide a written excuse letter to the faculty Office for Studies justifying his/her absence within five workdays of the teaching activity he/she was absent from.

Homework assignment

You need to score at least 60 % of overall points from the assignment. If you score less than 60 %, you get mark X and cannot sit the exam. Points from assignment carry over to the exam and are combined with your score from the test. Detailed assignment instructions are in the pdf published in "Study Materials".

Exam

Exam will be in the form of a test. If you score less than 50 % of points from the test, you get mark F. If you score at least 50 % points from the test, these points are then combined with points from your assignment to determine your mark.

- Enrolment Statistics (Spring 2020, recent)
- Permalink: https://is.muni.cz/course/fi/spring2020/MV013