MV013 Statistics for Computer Science

Fakulta informatiky
jaro 2020
Rozsah
2/2/0. 3 kr. (plus ukončení). Ukončení: zk.
Vyučující
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (přednášející)
Mgr. Jan Böhm (cvičící)
RNDr. Veronika Hajnová, Ph.D. (cvičící)
Mgr. Markéta Janošová (cvičící)
Mgr. Stanislav Zámečník (cvičící)
Garance
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Katedra teorie programování - Fakulta informatiky
Dodavatelské pracoviště: Přírodovědecká fakulta
Rozvrh
Po 17. 2. až Pá 15. 5. Út 8:00–9:50 D3
  • Rozvrh seminárních/paralelních skupin:
MV013/01: Po 17. 2. až Pá 15. 5. Čt 12:00–13:50 A215, S. Zámečník
MV013/02: Po 17. 2. až Pá 15. 5. Čt 14:00–15:50 A215, S. Zámečník
MV013/03: Po 17. 2. až Pá 15. 5. Út 16:00–17:50 A215, S. Zámečník
MV013/04: Po 17. 2. až Pá 15. 5. Po 10:00–11:50 A320, M. Janošová
MV013/05: Po 17. 2. až Pá 15. 5. Po 12:00–13:50 A320, M. Janošová
MV013/06: Po 17. 2. až Pá 15. 5. St 16:00–17:50 B130, J. Böhm
MV013/07: Po 17. 2. až Pá 15. 5. Čt 16:00–17:50 B204, V. Hajnová
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 37 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 including ANOVA 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), electronic test.
Vyučovací jazyk
Angličtina
Informace učitele
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.
Další komentáře
Studijní materiály
Předmět je vyučován každoročně.
Předmět je zařazen také v obdobích podzim 2015, podzim 2016, jaro 2018, jaro 2019, jaro 2021.
  • Statistika zápisu (nejnovější)
  • Permalink: https://is.muni.cz/predmet/fi/jaro2020/MV013