Bayesian networks
Course: Systems and methods of decision making
Structural unit: Faculty of Computer Science and Cybernetics
Title
Bayesian networks
Code
ОК.21
Module type
Обов’язкова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
1 Semester
Number of ECTS credits allocated
4
Learning outcomes
Know and understand the basic algorithms of logical-probabilistic derivation in Bayesian networks of trust and in algebraic Bayesian networks. Be able to apply the apparatus of Bayesian networks, using algorithms for eliminating variables and symbolic calculations, clustering and algorithms for propagating messages between network nodes.
Form of study
Full-time form
Prerequisites and co-requisites
Know: basics of mathematical analysis, algebra, probability theory, mathematical statistics. Be able to: formalize tasks and compile algorithms for the implementation of tasks. Have basic skills: working with stochastic objects.
Course content
Acquisition by students of basic knowledge about methods of modeling of processes of arbitrary nature, which take place in conditions of uncertainty, with the help of Bayesian networks, solving problems of forecasting, medical and technical diagnostics, making managerial decisions, automatic control.
Recommended or required reading and other learning resources/tools
4. Lebedev EA, Sharapov MM Course of lectures on probability theory. - Kyiv: Norita-Plus, 2007.
6. Neapolitan R. E. Learning Bayesian Networks. Pearson Prentice Hall, 2004.
7. Nguen H. T., Walker E. W. A First Course in Fuzzy Logic. - N.Y., London, Washington: Chapman & Hall / CRC, 2000. - 373 p.
Planned learning activities and teaching methods
Lecture, test work, independent work.
Assessment methods and criteria
Test work, exam.
Language of instruction
ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline