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Methods of Artificial Intelligence
Major: Distributed Information Systems and Technologies
Code of subject: 6.126.03.O.032
Credits: 4.00
Department: Information Systems and Networks
Lecturer: Roman Peleshchak
Semester: 6 семестр
Mode of study: денна
Завдання: - principles of operation of artificial intelligence;
- methodological approaches to constructing artificial intelligence systems;
- methodology of knowledge and technology engineering knowledge.
Learning outcomes: As a result of the discipline "Methods and systems of artificial intelligence" student should know:
- methods of knowledge representation;
- architecture of artificial intelligence;
- principles of operation of artificial intelligence;
- methodological approaches to constructing artificial intelligence systems;
- methodology of knowledge and technology engineering knowledge.
Required prior and related subjects: - discrete Math;
- the basics of programming;
- algorithmic language.
Summary of the subject: The course "Methods and systems of artificial intelligence" contains a description of the main methods of artificial intelligence and the use of these methods in solving practical problems.
Опис: The course "Methods and systems of artificial intelligence" contains a description of the main methods of artificial intelligence and the use of these methods in solving practical problems. Theoretical and practical skills of working with artificial intelligence methods make it possible to quickly solve real problems related to the complexity of processing and storing large arrays of heterogeneous information in intelligent information systems, use new information technologies and modern software products based on artificial intelligence methods intelligence in the work of various organizations, at enterprises and in everyday life.
Assessment methods and criteria: Diagnostics of knowledge takes place by evaluating the completed laboratory work and examination control (written and oral components) in the form of test questions.
Критерії оцінювання результатів навчання: Assessment methods include:
- current control (40%): written reports on laboratory work, oral questioning;
- final assessment (60% of exam): written and oral form.
Порядок та критерії виставляння балів та оцінок: 100–88 points – (“excellent”) is awarded for a high level of knowledge (some inaccuracies are allowed) of the educational material of the component contained in the main and additional recommended literary sources, the ability to analyze the phenomena being studied, in their relationship and development, clearly, succinctly, logically, consistently answer the questions, the ability to apply theoretical provisions when solving practical problems; 87–71 points – (“good”) is awarded for a generally correct understanding of the educational material of the component, including calculations, reasoned answers to the questions posed, which, however, contain certain (insignificant) shortcomings, for the ability to apply theoretical provisions when solving practical tasks; 70 – 50 points – (“satisfactory”) is awarded for weak knowledge of the component’s educational material, inaccurate or poorly reasoned answers, with a violation of the sequence of presentation, for weak application of theoretical provisions when solving practical problems; 49–26 points – (“not certified” with the possibility of retaking the semester control) is awarded for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to apply theoretical provisions when solving practical problems; 25-00 points - ("unsatisfactory" with mandatory re-study) is awarded for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to navigate when solving practical problems, ignorance of the main fundamental provisions.
Recommended books: 1. Nikolsky Yu.V., Pasichnyk V.V. Artificial intelligence systems. – Lviv, Magnolia-Plus, 2013. – 315 p.
2. Stuart J. Russell, Peter Norvig. Artificial intelligence. A modern approach. Third Edition. ISBN-10: 0-13-604259-7, ISBN-13: 978-0-13-604259-4
3. Machine learning: training manual / T.M. Basyuk, V.V. Lytvyn, L.M. Zakharia, N.E. Kunanets. - Lviv: "New World-2000", 2019. - 315 p.
4. Lytvyn V. V. Deep learning: a study guide / V. V. Lytvyn, R. M. Peleshchak, V. A. Vysotska. – Lviv: Publishing House of Lviv Polytechnic, 2021. – 264 c.
5. Burov E.V. Conceptual modeling of intelligent software systems: monograph / E.V. Burov.– Lviv: Publishing House of Lviv Polytechnic, 2012.– 432 p.
6. Lytvyn V.V. Designing information systems: a study guide / V. V. Lytvyn, N. B. Shakhovska. – Lviv: Magnolia 2006, 2017. – 380 c.
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