Artificial Intelligence in Information Systems

Major: Cyber Security
Code of subject: 8.125.00.M.30
Credits: 3.00
Department: Information Security
Lecturer: Professor Khoma V.V.
Semester: 3 семестр
Mode of study: денна
Learning outcomes: As a result of studying the discipline, the student should be able to demonstrate the following results: 1. Know the basic approaches to setting and solving problems in the field of intellectual systems. 2. Know the basic models and means of presenting knowledge. 3. Be able to design definitions of intelligent systems that are adequate to the task at hand. 4. Be able to transform the description of the situation into a task adequate to the problem statement. 5. Be able to choose the means of presentation of knowledge that are adequate to the task being solved. 6 Know the methods of formalization and interpretation of intellectual systems and their components. 7. Know the search methods; models and means of presenting knowledge (optional).
Required prior and related subjects: Fundamentals of Scientific Research and Organization of Science Computer methods of high-level design of security systems Actual scientific and applied problems in the field of information protection
Summary of the subject: This discipline belongs to the discipline cycle of specialty. The course deals with the subject of artificial intelligence systems based on the application of modern computer technologies and machine learning. The course allows students to gain insight into such a complex and multifaceted area of ??dynamically evolving knowledge as artificial intelligence (AI). The discipline includes two sections. The first general section includes three lectures, which discuss the history of the emergence and development of artificial intelligence systems, gives an overview of the areas covered in this course, methods of presentation and processing of knowledge, as well as the architecture and main components of AI systems. The second section, consisting of four lectures, focuses on the particular technologies that most often underpin real-world AI systems. First of all, it is an expert system and a knowledge-based system. The following lecture is devoted to neural networks and their learning algorithms. This topic is key to the course as the labs provide an in-depth study and practical exploration of this promising AI technology. Two major lectures provide information on the use of fuzzy sets to represent and process knowledge, as well as evolutionary algorithms, as one of the approaches used in AI.
Assessment methods and criteria: Current control: laboratory work (30), presentation of the abstract (10); Examination: written component (50), oral component (10).
Recommended books: 1. Russell S., Norvig P. Artificial intelligence: a modern approach, 3rd ed. - M .: Williams Publishing House, 2013. - 1408 p. 2. Lunger George F. Artificial intelligence: strategies and methods for solving complex problems. - M .: Williams, 2005. - 864 p. 3. Rutkovskaya D., Pilinsky M., Rutkovsky L. Neural networks, genetic algorithms and fuzzy systems. M .: Hotline-Telcom, 2013, 384 p. 4. Gary Riley, Joseph Jarratano. Expert Systems: Development Principles and Programming. - M .: Williams, 2006.-1152 p. 5. Haykin Simon. Neural Networks: Full Course 2nd Edition. - M .: Williams Publishing House, 2005. - 1104 p.