MV013 Statistics for Computer Science

Faculty of Informatics
Spring 2020
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
Mgr. Jan Böhm (seminar tutor)
RNDr. Veronika Eclerová, Ph.D. (seminar tutor)
Mgr. Markéta Janošová (seminar tutor)
Mgr. Stanislav Zámečník (seminar tutor)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Department of Computer Science – Faculty of Informatics
Supplier department: Faculty of Science
Timetable
Mon 17. 2. to Fri 15. 5. Tue 8:00–9:50 D3
  • Timetable of Seminar Groups:
MV013/01: Mon 17. 2. to Fri 15. 5. Thu 12:00–13:50 A215, S. Zámečník
MV013/02: Mon 17. 2. to Fri 15. 5. Thu 14:00–15:50 A215, S. Zámečník
MV013/03: Mon 17. 2. to Fri 15. 5. Tue 16:00–17:50 A215, S. Zámečník
MV013/04: Mon 17. 2. to Fri 15. 5. Mon 10:00–11:50 A320, M. Janošová
MV013/05: Mon 17. 2. to Fri 15. 5. Mon 12:00–13:50 A320, M. Janošová
MV013/06: Mon 17. 2. to Fri 15. 5. Wed 16:00–17:50 B130, J. Böhm
MV013/07: Mon 17. 2. to Fri 15. 5. Thu 16:00–17:50 B204, V. Eclerová
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 37 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 including ANOVA 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), electronic test.
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
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.

The course is also listed under the following terms Autumn 2015, Autumn 2016, Spring 2018, Spring 2019, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2020, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2020/MV013