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.
NI-VSM Selected statistical Methods Extent of teaching: 4P+2C Instructor: Hrabák P. Completion: Z,ZK Department: 18105 Credits: 7 Semester: L Annotation:
The course leads the student through advanced probabilistic and statistical methods used in information technology praxis. Particularly it deals with multivariate normal distribution, application of entropy in coding theory, hypothesis testing (T-tests, goodness of fit tests, independence test). Second part of the course deals with random processes with focus on Markov chains. The high point of the course is the Queuing theory and its application in networks.
Lecture syllabus:
21 MCMC
1. Summary of basic terms of probability theory 2. Random variables 3. Random vectors 4. Multivariate normal distribution 5. Entropy for discrete distribution 6. Application of entropy in coding theory 7. Entropy of continuous distribution 8. Summary of basic terms of statistics 9. Paired and Two-sample T-test, 10. Goodness of fit tests, 11. Independence test, contingency table 12. Estimation od PDF and CDF 13. Gaussian mixtures and EM algorithm 14. Random processes - stacionarity 15. Random processes - examples (Gaussian, Poisson) 16. Memory-less distributions, exponential race 17. Markov chain with discrete time 18. Markov chain with discrete time - state classiffication 19. Markov chain with discrete time - stationarity 20. Markov chain with discrete time - parameters estimation
22. Markov chain with continuous time 23. Markov chain with continuous time - Kolmogorov equations 24. Queuing theory, Little theorem 25. Queuing systems M/M/1 and M/M/m 26. Queuing systems M/G/infty Seminar syllabus:
1. Revision 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, sndependence test 7. Estimation od PDF and CDF 8. Random processes, Poisson 9. Markov chain with discrete time - stationarity 10. Markov chain with discrete time - state classiffication 11. Exponential race 12. Markov chain with continuous time 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, multivariable calculus, and linear algebra.
Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/MI-SPI/ The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester NI-PB.2020 Computer Security PP 2 NI-ZI.2020 Knowledge Engineering PP 2 NI-SPOL.2020 Unspecified Branch/Specialisation of Study PP 2 NI-TI.2020 Computer Science PP 2 NI-TI.2023 Computer Science PP 2 NI-SP.2023 System Programming PP 2 NI-WI.2020 Web Engineering PP 2 NI-SP.2020 System Programming PP 2 NI-SI.2020 Software Engineering (in Czech) PP 2 NI-MI.2020 Managerial Informatics PP 2 NI-PSS.2020 Computer Systems and Networks PP 2 NI-NPVS.2020 Design and Programming of Embedded Systems PP 2
Page updated 26. 4. 2024, semester: Z/2020-1, L/2021-2, L/2019-20, L/2022-3, Z/2019-20, L/2020-1, L/2023-4, Z/2022-3, Z/2021-2, Z/2023-4, Z/2024-5, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška