Course detail

Probability and Statistics

FIT-IPTAcad. year: 2025/2026

Classical probability. Axiomatic probability. Conditional probability. Total probability. Bayes' theorem. Random variable and random vector.  Characteristics of random variables and vectors. Discrete and continuous probability distributions. Central limit theorem. Transformation of random variables. Independence. Multivariate normal distribution. Descriptive statistics. Random sample. Point and interval estimates. Maximum likelihood method. Statistical hypothesis testing. Goodness-of-fit test. Analysis of variance. Correlation and regression analyses. Bayesian statistics.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Michal Fusek, Ph.D.

Department

Department of Mathematics (UMAT)

Entry knowledge

Secondary school mathematics and selected topics from previous mathematical courses.

Rules for evaluation and completion of the course

  • Homeworks: 20 points.
  • Final exam: 80 points. 

Class attendance. If students are absent due to medical reasons, they should contact their lecturer.

Aims

The main goal of the course is to introduce basic principles and methods of probability and mathematical statistics which are useful not only in computer sciences.
Acquired knowledge can be applied, for example, in other courses or in the BSc/MSc thesis.

Study aids

Not applicable.

Prerequisites and corequisites

Basic literature

Not applicable.

Recommended reading

Hlavičková, I., Hliněná, D.: Matematika 3. Sbírka úloh z pravděpodobnosti. VUT v Brně, 2015 (CS)
Montgomery, D. C., Runger, G. C.: Applied Statistics and Probability for Engineers. New York: John Wiley & Sons, 2011. (EN)

Classification of course in study plans

  • Programme BIT Bachelor's 2 year of study, winter semester, compulsory
  • Programme BIT Bachelor's 2 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Michal Fusek, Ph.D.
Mgr. Irena Hlavičková, Ph.D.

Syllabus

  1. Introduction to probability theory. Combinatorics and classical probability.
  2. Axiomatic probability. Conditional probability and independence. Probability rules. Total probability, Bayes' theorem.
  3. Random variable (discrete and continuous), probability mass function, cumulative distribution function, probability density function. Characteristics of random variables (mean, variance, skewness, kurtosis).
  4. Discrete probability distributions: Bernoulli, binomial, hypergeometric, geometric, Poisson.
  5. Continuous probability distributions: uniform, exponencial,  normal. Central limit theorem.
  6. Basic arithmetics with random variables and their influence on the parameters of probability distributions.
  7. Random vector (discrete and continuous). Joint and marginal probability mass function, cumulative distribution function, probability density function. Characteristics of random vectors (mean, variance, covariance, correlation coefficient). Independence. Multivariate normal distribution.
  8. Introduction to statistics. Descriptive statistics. Data processing. Characteristics of central tendency, variability and shape. Moments. Graphical representation of the data.
  9. Estimation theory. Point estimates. Maximum likelihood method. Bayesian inference.
  10. Interval estimates. Statistical hypothesis testing. One-sample and two-sample tests (t-test,  F-test).
  11. Goodness-of-fit tests.
  12. Introduction to regression analysis. Linear regression.
  13. Correlation analysies. Pearson's and Spearman's correlation coefficient.

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Michal Fusek, Ph.D.
Mgr. Irena Hlavičková, Ph.D.

Syllabus

Practising of selected topics of lectures.