Infopaket

Artificial intelligence methods

ОК 36

Обов’язкова дисципліна для ОП

First

2021/2022

5 Semester

6

The ability to demonstrate knowledge and understanding of the methods and principles of building artificial intelligence algorithms, developing software for intelligent systems. Knowledge of data processing methodology and intelligent data analysis methods, project support and debugging in Python environment. Ability to execute software projects for building artificial intelligence algorithms, organizing and formatting supporting documentation, summary and interim reports on project implementation. Ability to develop algorithms for intelligent data analysis based on computational intelligence methods, including large and unstructured data. Effective communication skills through precise argumentation. Ability to independently solve professional tasks.

Distance form

Studying disciplines such as “Algorithmization and basics of programming”, “Algorithms and data structures”, “Databases”, “Object-oriented programming”, “Operating systems”, “Information systems and technologies in enterprises”, “Software engineering”, “System analysis and decision making theory”, ability to use modern software tools for implementing artificial intelligence systems.

The course “Artificial intelligence methods” considers issues related to the application of modern methods and tools for designing, developing, and implementing intelligent systems with various functional purposes based on technologies such as neural networks, expert systems, computational intelligence, and machine learning. The goal of the discipline is to develop fundamental knowledge and skills in applying modern methods and tools for designing, developing, and implementing intelligent systems of various functional purposes based on technologies such as expert systems, neural networks, computational intelligence, machine learning, agent technologies, etc. and to acquire the skills of using such systems and technologies in practical work.

4. Advances in Intelligent Systems and Computing, 2021 https://drive.google.com/file/d/1tfRrzy8Om2XWSQLtcwbZGeaN7UolWnG6/view 5. Stepashko V., Bulgakova O., Zosimov V. Construction and research of the generalized iterative GMDH algorithm with active neurons. Advances in Intelligent Systems and Computing Vol. 689, 2018, Pages 492-510 Springer Verlag 6. Krotov, 2017 V. Krotov. The Internet of Things and new business opportunities Business Horizons, 60 (6) (2017), pp. 831-841 7. Bostrom N. Superintelligence. Paths, Dangers, Strategies / N. Bostrom. – Oxford University Press, 2016. – 432 p. 8. Advances in Intelligent Systems and Computing, 2019 9. https://drive.google.com/file/d/1CyjXhk8JVvlb4apbDbv95xXpo0WIvsvh/view 10. Advances in Intelligent Systems and Computing, 2020 https://drive.google.com/file/d/1x227JuzaLH8RiqkIBi9fYBcMX2Pni3P4/view

Lectures, laboratory activities, individual work

The level of achievement of all planned learning outcomes is determined based on the results of the defense of laboratory work and individual work. Semester evaluation of students is carried out throughout the semester from all types of work. The overall grade is formed as a weighted sum of points earned by the student for various types of work. The results of students' educational activities during the semester are evaluated on a 100-point scale. The work in the semester is divided into two content modules, which include two modular control works and nine laboratory works - maximum of 60 points (minimum of 36 points). The form of final assessment is an exam that includes theoretical questions and practical tasks - maximum of 40 points, but no less than 24 points.

Ukrainian