A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.
BIK-PST.21 Probability and Statistics Extent of teaching: 14KP+4KC Instructor: Hrabák P., Novák P., Vašata D. Completion: Z,ZK Department: 18105 Credits: 5 Semester: Z Annotation:
Students will learn the basics of probabilistic thinking, the ability to synthesize prior and posterior information and learn to work with random variables. They will be able to apply basic models of random variable distributions and solve applied probabilistic problems in informatics and computer science. Using the statistical induction they will be able to perform estimations of unknown distributional parameters from random sample characteristics. They will also be introduced to the methods for testing statistical hypotheses and determining the statistical dependence of two or more random variables.
Lecture syllabus:
1. Probability: Random event, event space structure, probability of a random event and its basic properties. Conditional probability: Dependent and independent events, Bayes theorem. 2. Random variable: Distribution function of a random variable, continuous and discrete distributions, quantiles, median. Characteristics of position and shape: Mean value, variance, general moments, kurtosis and skewness. 3. Overview of basic distributions: binomial, Poisson, uniform, normal, exponential. Their basic properties. Probability applications. Hash functions, probabilistic algorithms. 4. Random vector: Joint and marginal statistics, correlation coefficient, dependence and independence of random variables. Descriptive statistics: Classification and processing of data sets, characteristics of position, variance, and shape, sampling moments, graphical representation of data. 5. Random sampling: Simple and stratified sampling, their distributions, basic sampling statistics, sample mean and variance, distributions (t-distribution, F-distribution, chi square). Parameter estimation: Confidence interval, point estimation, methods. 6. Hypothesis testing: Testing strategy, mean value and variance tests, some of their modifications. Application of statistical testing in CS. Non-parametric tests: Comparing distributions, Wilcoxon test, Smirnov-Kolmogorov test, goodness-of-fit test. 7. Analysis of variance: One-way and two-way classification, normality testing. Correlation and regression analysis: Linear and quadratic regression, sample correlation. Seminar syllabus:
1. Elements of probability. Conditional probability. Random variable. Basic characteristics of random variables. Using basic distributions. Calculations of random variable characteristics. Hash functions. 2. Multidimensional random variables. Processing of sets of data. Random sampling. Parameter estimation. Hypotheses testing. Non-parametric tests. Correlation analysis. Literature:
1. Ahn, H. Probability and Statistics for Science and Engineering with Examples in R. Cognella, 2017. ISBN 978-1516513987. 2. Zvára, K., Štěpán, J. Pravděpodobnost a matematická statistika (5.vydání). Matfyzpress, 2013. ISBN 978-8073782184. 3. Johnson, J. L. Probability and Statistics for Computer Science. Wiley-Interscience, 2008. ISBN 470383429. 4. Bonselet, Ch. Probability, Statistics, and Random Signals. Oxford University Press, 2016. ISBN 978-0190200510. 5. Grimmett, G. R., Stirzaker, D. R., Probability and Random Processes (3rd Edition). Oxford University Press, 2001. ISBN 0-19-857223-9. Requirements:
Basics of combinatorics and mathematical analysis.
The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester BIK-IB.21 Information Security 2021 (in Czech) PP 5 BIK-SPOL.21 Unspecified Branch/Specialisation of Study PP 5 BIK-PV.21 Computer Systems and Virtualization 2021 (in Czech) PP 5 BIK-PS.21 Computer Networks and Internet 2021 (in Czech) PP 5 BIK-SI.21 Software Engineering 2021 (in Czech) PP 5
Page updated 19. 4. 2024, semester: L/2020-1, L/2021-2, Z/2023-4, Z/2024-5, Z/2019-20, Z/2022-3, L/2019-20, L/2022-3, Z/2020-1, Z/2021-2, L/2023-4, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška