Artificial intelligence
Course: Systems and methods of decision making
Structural unit: Faculty of Computer Science and Cybernetics
Title
Artificial intelligence
Code
ДВС.3.02.01
Module type
Вибіркова 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
Know the basic models, methods and algorithms used to build artificial intelligence systems. Know the theory and technology of machine learning.
Be able to develop artificial intelligence systems using modern technologies and programming languages (specialized libraries for the development of models of machine learning, natural language processing, etc.).
Form of study
Full-time form
Prerequisites and co-requisites
1. Know: basics of the disciplines "Programming", "Discrete Mathematics", "Probability Theory", "Construction and analysis of algorithms".
2. Be able to use information technology and programming languages to solve applied problems and conduct research.
3. Have basic skills in compiling and analyzing algorithms and programming in Python, Java, C ++.
Course content
The discipline " Artificial Intelligence" is part of the educational-professional training program for educational qualification level "Master" of knowledge 12 "Information Technology", specialty 124 "Systems Analysis", educational program "Systems and methods of decision making». It is a basic discipline of higher education institutions specializing in information technology, as well as an effective tool for solving scientific and engineering problems. The purpose and objectives of the discipline is to get acquainted with one of the main scientific areas in the field of computer technology " Artificial Intelligence " and master the technology of solving a wide range of problems of science and technology (including artificial Intelligence problems) using methods, approaches, and algorithms of AI.
Recommended or required reading and other learning resources/tools
Nirenburg S., Raskin V. Ontological Semantics, 2001,//crl. nmsu. edu/stuff. pages/Techial/book/index-book. Html
Miller, G., Wordnet: An online lexical database, International Journal of Lexicography, 3 (4), 1990.
Pusteyovsky James. The Generative Lexicon. p. 69-72. MIT, London.
Planned learning activities and teaching methods
Lecture, laboratory work, individual work.
Assessment methods and criteria
Test work, defense of laboratory work, semester test.
Language of instruction
ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline