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Artificial Intelligence Technologies in Computer and Cyber-Physical Systems
Major: Computer Systems and Networks
Code of subject: 7.123.01.O.005
Department: Electronic Computing Machines
Lecturer: Professor Kuryliak Dozyslav Bohdanovych
Semester: 1 семестр
Mode of study: денна
Завдання: The study of an educational discipline involves the formation of competencies in students of education: integral competence: INT - the ability to solve complex specialized tasks and practical problems during professional activities in the field of information technologies or in the learning process, which involves the application of theories and methods of computer engineering and are characterized by complexity and uncertainty of conditions.
Learning outcomes: know: fields of applications of CSAI, knowledge base, models of knowledge representation; logical models of knowledge representation in CSAI; the resolution method; fuzzy sets; methods of derivation under fuzzy conditions; semantic networks; production models; frames; artificial neural networks; methods of making decision; recognition of images; types of problems recognition; methods and algorithms of classification of images; theoretical aspects of machine translation; levels of understanding and their classification; specifications of design and exploiting of expert systems
Required prior and related subjects: prerequisites: - computer systems; - systems programming; - technologies of computer systems designing
Summary of the subject: Modern methods of CSAI and their application; Methods of database knowledge formation; Operation with knowledge models; Application for CSAI designing
Опис: Supervised machine learning Unsupervised machine learning Compuetr vision Natural language processing Time series forecasting Reinforcement learning
Assessment methods and criteria: Final control (exam), writing (50%), oral form (50%).
Критерії оцінювання результатів навчання: The semester grade is issued on the condition that the student completes the study plan. The semester grade is formed from the results of current monitoring of laboratory work and semester testing. The result of the semester test is the product of the result of the semester test in the virtual learning environment and the coefficient of the lecture tests in the virtual learning environment. The maximum score in points is 100. Current control (lab. Works) - 40. Credit control: written component - 50, oral component - 10.
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 interrelationship 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”) 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. Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, 4th edition, Pearson, 2020. - 1136 p. 2. Richard S. Sutton, Andrew G. Barto, Reinforcement Learning: An Introduction, A Bradford Book, 2 ed., MIT Press, Cambridge, MA, 2018. - 322 p. 3. Multiagent Systems, by Gerhard Weiss (Editor), 2nd edition, The MIT Press, 2013. - 920 p. 4. Adrian A. Hopgood, Intelligent Systems for Engineers and Scientists: A Practical Guide to Artificial Intelligence, 4th ed., CRC Press, 2021. – 515 p. 5. Narendra, K. and Thathachar, M. A. L., Learning Automata: An Introduction, 2nd ed., Dover Publications, 2013. - 496 p. 6. K. Najim, A.S. Poznyak, Learning Automata: Theory and Applications, Elsevier, 2014. – 236 p. 7. Chowdhary, Chiranji Lal, Intelligent systems: advances in biometric systems, soft computing, image processing, and data analytics, Apple Academic Press, 2020. – 320 p. 8. Maxim Lapan, Deep Reinforcement Learning Hands-On, 2nd edition, Packt Publishing, 2020. - 798 p. 9. Richard E. Neapolitan, Xia Jiang, Artificial Intelligence: With an Introduction to Machine Learning, Chapman and Hall, 2018. - 480 p. 10. Laurence Moroney, AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence, O'Reilly Media, 2020. - 390 p. 11. Leon Reznik, Intelligent Security Systems: How Artificial Intelligence, Machine Learning and Data Science Work For and Against Computer Security, Wiley-IEEE Press, 2021. – 371 p. 12. Artificial Intelligence-based Internet of Things Systems, Souvik Pal, Debashis De, Rajkumar Buyya (eds.), Springer, 2022. – 513 p.
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