MV013 Statistics for Computer Science

Fakulta informatiky
jaro 2019
Rozsah
2/2/0. 4 kr. (plus ukončení). Doporučované ukončení: zk. Jiná možná ukončení: k, z.
Vyučující
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (přednášející)
Mgr. Markéta Janošová (cvičící)
Garance
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Fakulta informatiky
Dodavatelské pracoviště: Přírodovědecká fakulta
Rozvrh
Út 19. 2. až Út 14. 5. Út 10:00–11:50 B204
  • Rozvrh seminárních/paralelních skupin:
MV013/01: St 12:00–13:50 A215, M. Janošová
MV013/02: St 14:00–15:50 A215, M. Janošová
Předpoklady
The knowledge of basic calculus, linear algebra and theory of probability.
Omezení zápisu do předmětu
Předmět je nabízen i studentům mimo mateřské obory.
Mateřské obory/plány
předmět má 22 mateřských oborů, zobrazit
Cíle předmětu
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.
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 on real continuous data;
- to implement statistical methods of continuous and discrete data to R.
Osnova
  • 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
Literatura
  • CASELLA, George a Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 0534243126. info
Výukové metody
Lectures, practicals.
Metody hodnocení
Homework (project), oral exam.
Vyučovací jazyk
Angličtina
Další komentáře
Studijní materiály
Předmět je vyučován každoročně.
Předmět je zařazen také v obdobích podzim 2015, podzim 2016, jaro 2018, jaro 2020, jaro 2021, jaro 2022, jaro 2023, jaro 2024, jaro 2025.