FI:MV013 Statistics for CS - Course Information

MV013 Statistics for Computer Science

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).

Teacher(s)

RNDr. Radim Navrátil, Ph.D. (lecturer)
Reza Dastranj, MSc (seminar tutor)
Mgr. Pavel Morcinek (seminar tutor)

Guaranteed by

RNDr. Radim Navrátil, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
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
• Testing hypotheses about one-sample
• Testing hypotheses about two-samples
• 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.

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