Probability theory and mathematical statistics
Course: «Applied (computer) Linguistics and English language»
Structural unit: Educational and Scientific Institute of Philology
Title
Probability theory and mathematical statistics
Code
ННД. 11.04
Module type
Обов’язкова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
4 Semester
Number of ECTS credits allocated
3
Learning outcomes
PLO 3. To organize the process of learning and self-education.
PLO 18. To have skills for managing projects and solving complicated issues in professional activity in the field of computational linguistics and be responsible for making decisions under unpredictable circumstances.
PLO 22. To know the basic Mathematics conceptions and terminology and Mathematic methods and use them to solve specialized tasks of computational linguistics
PLO 24. To know and to implement fundamental conceptions, paradigms, and the main functioning principles of language, instrumental and computing means of software engineering.
PLO 27. To know and use relevant mathematical conceptions, methods of domain, system and object-oriented analyses, and mathematical simulation, modern programming languages for creating software.
Form of study
Full-time form
Prerequisites and co-requisites
To know the basics of higher mathematics, discrete mathematics, mathematical logic, and the theory of algorithms. To be able to formalize tasks and compile algorithms for the implementation of tasks. To have basic skills in working with stochastic objects.
Course content
Acquisition by students of basic knowledge about stochastic experiments, the ability to work with basic statistical models, and skills of applying the acquired knowledge to practical problems that require probabilistic and statistical analysis.
Recommended or required reading and other learning resources/tools
Gusak D., Kukush A., Kulik A. Mishura Yu. Pilipenko A. Theory of Stochastic Processes with Applications to Financial Mathematics and Risk Theory.New York: Springer, 2008, 375 p.
Planned learning activities and teaching methods
Lecture, practical classes, control work, independent work.
Assessment methods and criteria
Test work, current activity assessment, credit.
Language of instruction
Ukrainіаn
Lecturers
This discipline is taught by the following teachers
Igor
Anatoliyovych
Makushenko
Applied Statistics
Faculty of Computer Science and Cybernetics
Faculty of Computer Science and Cybernetics
Departments
The following departments are involved in teaching the above discipline
Applied Statistics
Faculty of Computer Science and Cybernetics