MV013 Statistics for Computer Science

Fakulta informatiky
jaro 2024
Rozsah
2/2/0. 3 kr. (plus ukončení). Doporučované ukončení: zk. Jiná možná ukončení: z.
Vyučující
Reza Dastranj, MSc (cvičící)
Mgr. Pavel Morcinek (cvičící)
Garance
Ústav matematiky a statistiky – Ústavy – Přírodovědecká fakulta
Dodavatelské pracoviště: Ústav matematiky a statistiky – Ústavy – Přírodovědecká fakulta
Rozvrh
Út 8:00–9:50 D3
• Rozvrh seminárních/paralelních skupin:
MV013/01: Po 16:00–17:50 A215, P. Morcinek
MV013/02: Po 18:00–19:50 A215, P. Morcinek
MV013/03: St 8:00–9:50 A320, R. Dastranj
MV013/04: St 10:00–11:50 A320, R. Dastranj
MV013/05: Pá 8:00–9:50 A320, R. Dastranj
MV013/06: Pá 10:00–11:50 A320, R. Dastranj
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 nabízen i studentům mimo mateřské obory.
Mateřské obory/plány
předmět má 37 mateřských oborů, zobrazit
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
• 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
Studijní materiály
Předmět je vyučován každoročně.
Předmět je zařazen také v obdobích podzim 2015, podzim 2016, jaro 2018, jaro 2019, jaro 2020, jaro 2021, jaro 2022, jaro 2023, jaro 2025.
• Statistika zápisu (nejnovější)