Infopaket

Probability Theory for Computer Science

ОК 16

Обов’язкова дисципліна для ОП

First

2022/2023

3 Semester

5

Use knowledge of regularities of random phenomena, their properties and operations, models of random processes and modern software environments to solve problems of statistical data processing and build predictive models. To know and apply the tools of data analysis, methods of cloud computing, and artificial intelligence to solve practical tasks, including the analysis of time series, statistical problems, determining probabilistic relationships between data, and economic problems

Full-time form

1. Proficiency in skills and knowledge of mathematical analysis and discrete mathematics. 2. Ability to independently work with scientific and scientific-methodological mathematical literature.

"Probability Theory for Computer Science" covers fundamental concepts of probability theory, mathematical statistics, sample space of elementary events, operations with events, probability of an event, conditional probability, probability properties, formulas of total probability, Bayes' formulas, random variables, Bernoulli's scheme, binomial distribution, probability density, uniform distribution, normal distribution, exponential distribution, mathematical expectation, variance, correlation, sample set, sample mean, sample variance, sample correlation coefficient, linear regression, parameter estimation methods, hypothesis testing methods, methods of constructing confidence intervals. These basic mathematical concepts are essential for preparing students to use probability theory methods in building mathematical models in computer science through statistical methods. The application of probability methods for problem-solving in information technology aims to develop skills for independent use and study of literature on probability theory.

1. Barkovskyi V., Barkovska N., Lopatin O. Probability Theory and Mathematical Statistics. – K. Center for Educational Literature, 2019 – 424 p. 2. Vyhodner I.V., Bylousova T.P., Liakhovych T.P. Probability Theory and Mathematical Statistics. – Helvetika, 2019 – 336 p. 3. Pryluts'kyi Yu.I., Ilchenko O.V., Tsymbaliuk O.V., Kosterin S.O. Statistical Methods in Biology: textbook – K.: Naukova Dumka, 2017 – 233 p. 4. Naiko D.A. Shevchuk O.F. Probability Theory and Mathematical Statistics: textbook. – Vinnytsia: VNAU, 2020 – 382 p. 5. Karmeliuk H. Probability Theory and Mathematical Statistics: textbook. – K.: Center for Educational Literature, 2019 – 576 p. 6. Zaitsev Ye.P. Probability Theory and Mathematical Statistics: textbook. – Alerta, 2017 – 440 p. 7. Byshevets N.H. Probability Theory and Mathematical Statistics using MS Excel spreadsheet: textbook. – Helvetika, 2021 – 234 p.

Lectures, practical work, individual work

Semester Evaluation: - Control work on the topic "Calculation of means, variances, and correlation coefficients" – 10 points (threshold score 6 points), - Control work on the topic "Construction of confidence intervals for parameters of the normal distribution" – 10 points (threshold score 6 points), - Assessment for the completion and defense of practical work during practical sessions – 40 points (threshold score 24 points). Final assessment in the form of an exam: maximum score 40 points, threshold score 24 points. During the exam, the student takes a test and answers questions. Content modules (CM) contribute to the scores assigned based on the student's performance throughout the semester, accumulating points for systematic work during the semester. The final assessment is the sum of points for content modules and points for the exam.

Ukrainian