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.
NIE-VSM Selected statistical Methods Extent of teaching: 4P+2C Instructor: Novák P. Completion: Z,ZK Department: 18105 Credits: 7 Semester: L Annotation:
Summary of probability theory; Multivariate normal distribution; Entropy and its application to coding; Statistical tests: T-tests, goodness of fit tests, independence test; Random processes - stacionarity; Markov chains and limiting properties; Queuing theory
Lecture syllabus:
21 MCMC
1. Summary of basic terms of probability theory 2. Random variables 3. Random vectors 4. Multivariate normal distribution 5. Entropy of discrete distributions 6. Application of entropy in coding theory 7. Entropy of continuous distributions 8. Summary of basic notions of statistics 9. Paired and Two-sample T-test, 10. Goodness of fit tests, 11. Independence testing, contingency tables 12. Estimation of PDF and CDF 13. Gaussian mixtures and EM algorithm 14. Random processes - stationarity 15. Random processes - examples (Gaussian, Poisson) 16. Memory-less distributions, exponential race 17. Discrete-time Markov chains - introduction 18. Discrete-time Markov chains - classification of states 19. Discrete-time Markov chains - stationarity 20. Discrete-time Markov chains - estimation of parameters
22. Continuous time Markov chains - introduction 23. Continuous time Markov chains - Kolmogorov equations 24. Queuing theory, Little's theorem 25. Queuing systems M/M/1 and M/M/m 26. Queuing systems M/G/infinity Seminar syllabus:
1. Review lesson: basics of probability 2. Random vectors, multivariate normal distribution 3. Entropy and coding theory 4. Entropy, mutual information 5. T-tests 6. Goodness of fit tests, independence test 7. Estimation of PDF and CDF 8. Random processes, Poisson process 9. Discrete-time Markov chains - stationarity 10. Discrete-time Markov chains - classification of states 11. Exponential race 12. Continuous-time Markov chains 13. Queuing theory Literature:
1. Cover, T. M. - Thomas, J. A. : Elements of Information Theory (2nd Edition). Wiley, 2006. ISBN 978-0-471-24195-9. 2. Durrett, R. : Essentials of Stochastic Processes. Springer, 1999. ISBN 978-0387988368. 3. Grimmett, G. - Stirzaker, D. : Probability and Random Processes (3rd Edition). Oxford University Press Inc., 2001. ISBN 978-0-19-857222-0. Requirements:
Basics of probability and statistics, multivariate calculus, and linear algebra.
Course information and study materials to be found at https://courses.fit.cvut.cz/NIE-VSM/ The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester NIE-DBE.2023 Digital Business Engineering PP 2 NIE-SI.21 Software Engineering 2021 PP 2 NIE-TI.21 Computer Science 2021 PP 2 NIE-NPVS.21 Design and Programming of Embedded Systems 2021 PP 2 NIE-PSS.21 Computer Systems and Networks 2021 PP 2 NIE-PB.21 Computer Security 2021 PP 2
Page updated 25. 4. 2024, semester: Z,L/2023-4, Z/2019-20, Z/2024-5, L/2022-3, Z/2020-1, Z,L/2021-2, L/2020-1, Z/2022-3, L/2019-20, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška