FI:MV013 Statistics for CS - Informace o předmětu

MV013 Statistics for Computer Science

Rozsah

2/2/0. 3 kr. (plus ukončení). Doporučované ukončení: zk. Jiná možná ukončení: z.
Vyučováno kontaktně

Vyučující

prof. Mgr. Petr Hasil, Ph.D. (přednášející)
RNDr. Radim Navrátil, Ph.D. (přednášející)
Reza Dastranj, MSc (cvičící)
Mgr. Pavel Morcinek (cvičící)

Garance

prof. Mgr. Petr Hasil, Ph.D.
Ústav matematiky a statistiky – Ústavy – Přírodovědecká fakulta
Dodavatelské pracoviště: Ústav matematiky a statistiky – Ústavy – Přírodovědecká fakulta

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je otevřen studentům libovolného oboru.

Cíle předmětu

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.

Výstupy z učení

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.

Osnova

• 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

Literatura

• WASSERMAN, Larry. All of statistics : a concise course in statistical inference. New York: Springer, 2004, xix, 442. ISBN 9780387402727. info
• CASELLA, George a Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 0534243126. info

Výukové metody

Lectures, practical exercise classes with computers.

Metody hodnocení

Homeworks and tests during the semester (40 points), final written exam (60 points). At least 50 % of averall points is needed to pass.

Vyučovací jazyk

Angličtina

Informace učitele

Capacity of the course is limited. Registration is required.

Další komentáře

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• Statistika zápisu (nejnovější)
  
• Permalink: https://is.muni.cz/predmet/fi/jaro2025/MV013