MV013 Statistics for Computer Science

Fakulta informatiky
jaro 2025
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)
Mgr. Markéta Makarová (cvičící)
Mgr. Pavel Morcinek (cvičící)
Mgr. Ondřej Pokora, Ph.D. (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
Mathematical analysis: functions, limits of sequences and functions, derivatives and integrals of real and multidimensional functions.
Linear algebra: matrices and determinants, eigenvalues and eigenvectors.
Probability theory: probability, random variables and vectors, their distributions and properties, limit theorems.
Statistics: Descriptive statistics, introduction to hypotheses testing, confidence intervals, linear regression model
Omezení zápisu do předmětu
Předmět je otevřen studentům libovolného oboru.
Cíle předmětu
The aim of the course is to review and deepen fundamental knowledge of statistics so that students, upon completing the course, are able to process real-world data correctly and draw accurate results and conclusions from them. We will focus on advanced statistical methods that can be applied in situations where standard assumptions about data, such as normality or homoscedasticity, are not met.
Výstupy z učení
Student will:
learn to select and apply appropriate statistical models for different types of data, enhancing their ability to analyze and solve real-world problems across various fields;
develop critical thinking skills to evaluate and interpret statistical models, ensuring they can make data-driven decisions and draw valid conclusions;
understand the assumptions underlying statistical models and how to address issues such as model mis-specification, allowing them to handle non-ideal data conditions effectively;
be able to communicate statistical findings clearly through reports, visualizations, and presentations, making complex statistical concepts accessible to non-experts;
enhance their skills in statistical software R, enabling them to efficiently manage and analyze datasets using modern computational tools.
Osnova
  • Why do we need statistics?
  • Meeting the data
  • Nonparametric and parametric statistical models
  • From point estimates to confidence intervals
  • Alternatives to one sample t-test - parametric and nonparametric tests, bootstrapping
  • Normality of data and how to identify it
  • From one sample to two and even more samples
  • Linear regression models from statistical point of view
  • Finding connections - testing for independence
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 in the statistical software R.
Metody hodnocení
Homework, ROPOT 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
Předmět je vyučován každoročně.
Výuka probíhá každý týden.
Předmět je zařazen také v obdobích podzim 2015, podzim 2016, jaro 2018, jaro 2019, jaro 2020, jaro 2021, jaro 2022, jaro 2023, jaro 2024.
  • Statistika zápisu (nejnovější)
  • Permalink: https://is.muni.cz/predmet/fi/jaro2025/MV013