## MV013 Statistics for Computer Science

Faculty of Informatics
Spring 2024
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Taught in person.
Teacher(s)
Reza Dastranj, MSc (seminar tutor)
Mgr. Pavel Morcinek (seminar tutor)
Guaranteed by
prof. RNDr. Jan Slovák, DrSc.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 8:00–9:50 D3
• Timetable of Seminar Groups:
MV013/01: Mon 16:00–17:50 A215, P. Morcinek
MV013/02: Mon 18:00–19:50 A215, P. Morcinek
MV013/03: Wed 8:00–9:50 A320, R. Dastranj
MV013/04: Wed 10:00–11:50 A320, R. Dastranj
MV013/05: Fri 8:00–9:50 A320, R. Dastranj
MV013/06: Fri 10:00–11:50 A320, R. Dastranj
Prerequisites
Basic knowledge of mathematical analysis: functions, limits of sequences and functions, derivatives and integrals of real and multidimensional functions.
Basic knowledge of linear algebra: matrices and determinants, eigenvalues and eigenvectors.
Basic knowledge of probability theory: probability, random variables and vectors, limit theorems.
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 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; 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
• What is statistics? Motivation and examples.
• Exploratory data analysis
• Revision of probability theory
• Parametric models - methods for parameter estimation
• Confidence intervals and hypothesis testing
• ANOVA
• Testing for independence
• Nonparametric tests
• Linear regression models
Literature
• WASSERMAN, Larry. All of statistics : a concise course in statistical inference. New York: Springer, 2004, xix, 442. ISBN 9780387402727. info
• CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 0534243126. info
Teaching methods
Lectures, practical exercise classes with computers.
Assessment methods
Homeworks and tests during the semester (40 points), final written exam (60 points). At least 50 % of averall points is needed to pass.
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
Capacity of the course is limited. Registration is required.
The course is also listed under the following terms Autumn 2015, Autumn 2016, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023.
• Enrolment Statistics (recent)