Fundamentals of artificial intelligence

Course: Applied mathematics

Structural unit: Faculty of Computer Science and Cybernetics

Title
Fundamentals of artificial intelligence
Code
ННД.04
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
3 Semester
Number of ECTS credits allocated
3
Learning outcomes
PLO5. Be able to carry out effective communicative activities of the project development team. PLO7. Be able to organize, configure, and develop web systems using the principles of distributed systems, hypertext systems, appropriate hardware, and software
Form of study
Full-time form
Prerequisites and co-requisites
To successfully study the discipline the student must meet the following requirements: 1. Know: basic information on mathematical analysis and set theory, linear algebra, methods of estimation and identification, mathematical modeling and modeling of dynamic systems of programming theory, mathematical logic, mathematical statistics 2. Be able to: solve systems of linear algebraic equations with parameters, apply various optimization methods to estimate parameters (eg least squares method, gradient descent type methods), and skillfully use the concepts of set theory and optimization methods. 3. Have the skills to build, analyze, and apply mathematical models in solving applied problems.
Course content
The purpose of the discipline "Fundamentals of Artificial Intelligence" is to master the methodology of solving problems based on methods of artificial intelligence in the analysis and design of information-control systems and related technologies.
Recommended or required reading and other learning resources/tools
1. Stuart J Russel, Peter Norvig. Artificial Intelligence. A modern Approach. – Prentice Hall, New Jersey. – 2003.
Planned learning activities and teaching methods
Lectures, independent work.
Assessment methods and criteria
Semester assessment: The maximum number of points that can be obtained by a student is 60 points: Test work №1: - 20/12 points. Test work № 2: - 20/12 points. Current evaluation - 20/12 points. Final assessment in the form of an exam.
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Andriy V. Shatyrko
Complex systems modelling
Faculty of Computer Science and Cybernetics

Departments

The following departments are involved in teaching the above discipline

Complex systems modelling
Faculty of Computer Science and Cybernetics