MDA302 Probability

Faculty of Science
Autumn 2025
Extent and Intensity
0/0/0. 8 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Jan Koláček, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Jan Koláček, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites
PROGRAM(B-DAE)
Calculus in one and several variables (MDA201), basics of linear algebra (MDA101)
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Introductory course to educate students in descriptive statistics, theory of probability, random variables and probabilistic distributions, including data visualization.
Learning outcomes
Students will understand chance as the mathematical model for limited knowledge;
understand theoretical foundations of probability with a view towards statistics;
be familiar with random variables and vectors, probability distributions and their properties;
be able to relate probability concepts to real-world phenomena;
be able to solve probabilistic tasks both mathematically and practically;
be able to visualize and explore data in R statistical software
Syllabus
  • Probability, random variables and vectors and their characteristics.
  • Statistical calculus, simulation, limit theorems, descriptive statistics and data visualization, exploratory statistics, kernel smoothing, principal component analysis.
Literature
    recommended literature
  • Supplementary materials: Jan Slovák et al, Brisk Guide to Mathematics, www.math.muni.cz/~slovak /BG.pdf, Chapter 10
  • JAMES, Gareth R.; Daniela WITTEN; Trevor HASTIE and Robert TIBSHIRANI. An introduction to statistical learning : with applications in R. Second edition. New York: Springer, 2021, xv, 607. ISBN 9781071614174. info
  • WASSERMAN, Larry. All of statistics : a concise course in statistical inference. New York: Springer, 2010, xi, 442. ISBN 9780387217369. info
Teaching methods
Essentially asynchronous approach; complemented by synchronous communication with the tutor upon agreement
Assessment methods
Written and oral exam. The written exam is for max 40 points. For successful examination (the grade at least E), the student needs in total 18 points or more.
Language of instruction
English
Follow-Up Courses
Study support
https://is.muni.cz/auth/el/sci/podzim2025/MDA302/index.qwarp
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2025/MDA302/index.qwarp
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