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.
BI-PST Probability and Statistics Extent of teaching: 2P+1R+1C Instructor: Completion: Z,ZK Department: 18105 Credits: 5 Semester: Z Annotation:
The 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 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 of determining the statistical dependence of two or more random variables.
Lecture syllabus:
1. Probability: Random events, event space structure, probability of a random event and its basic properties. 2. Conditional probability: Dependent and independent events, Bayes theorem. 3. Random variables: Distribution function of a random variable, continuous and discrete distributions, quantiles, median. 4. Characteristics of random variables: Expected value, variance, general moments, kurtosis and skewness. 5. Overview of basic distributions: binomial, geometric, Poisson, uniform, normal, exponential. Their basic properties. 6. Random vectors: Joint and marginal statistics, correlation coefficient, dependence and independence of random variables. 7. Random vectors: Conditional distributions, sums of random variables. 8. Limit theorems: Laws of large numbers, central limit theorem 9. Statistical estimation: Classification and processing of data sets, characteristics of position, variance and shape, sampling moments, graphical representation of data. 10. Point estimation: Random sample, basic sample statistics, sample mean and variance, distributions (t-distribution, F-distribution, chi square). 11. Interval estimation: Confidence intervals for expectation and variance. 12. Hypothesis testing: Testing strategy, tests for expectation and variance, their modifications. Application of statistical testing in CS. 13. Correlation and regression analysis: Linear and quadratic regression, sample correlation. Seminar syllabus:
1. Basics of probability. 2. Conditional probability. 3. Random variables. 4. Basic characteristics of random variables. 5. Using basic distributions. 6. Random vectors - independence, covariance. 7. Random vectors - conditional distributions and sums. 8. Limit theorems 9. Processing of sets of data. 10. Statistical point estimation. 11. Interval estimation. 12. Hypotheses testing. 13. Regression and correlation analysis. Literature:
1. Johnson, J. L. Probability and Statistics for Computer Science. Wiley-Interscience, 2008. ISBN 0470383429. 2. Li, X. R. Probability, Random Signals, and Statistics. CRC, 1999. ISBN 0849304334. Requirements:
Basics of combinatorics and mathematical analysis.
Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/BI-PST/ The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester BI-SPOL.2015 Unspecified Branch/Specialisation of Study PP 5 BI-WSI-PG.2015 Web and Software Engineering PP 5 BI-WSI-WI.2015 Web and Software Engineering PP 5 BI-WSI-SI.2015 Web and Software Engineering PP 5 BI-ISM.2015 Information Systems and Management PP 5 BI-ZI.2018 Knowledge Engineering PP 5 BI-PI.2015 Computer engineering PP 5 BI-TI.2015 Computer Science PP 5 BI-BIT.2015 Computer Security and Information technology PP 5
Page updated 16. 4. 2024, semester: Z/2024-5, L/2021-2, Z,L/2022-3, Z/2019-20, L/2023-4, L/2019-20, L/2020-1, Z/2021-2, Z/2023-4, Z/2020-1, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška