MV013 Statistics for Computer Science

Faculty of Informatics
Spring 2019
Extent and Intensity
2/2/0. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
Mgr. Markéta Janošová (seminar tutor)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Faculty of Informatics
Supplier department: Faculty of Science
Timetable
Tue 19. 2. to Tue 14. 5. Tue 10:00–11:50 B204
  • Timetable of Seminar Groups:
MV013/01: Wed 12:00–13:50 A215, M. Janošová
MV013/02: Wed 14:00–15:50 A215, M. Janošová
Prerequisites
The knowledge of basic calculus, linear algebra and theory of probability.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 22 fields of study the course is directly associated with, display
Course objectives
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.
Learning outcomes
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.
Syllabus
  • 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
Literature
  • CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 0534243126. info
Teaching methods
Lectures, practicals.
Assessment methods
Homework (project), oral exam.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2015, Autumn 2016, Spring 2018, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2019, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2019/MV013