Semester(s), when the module is taught 3
Responsible for module person
Language of study Kazakh/Russian/English
Relationship with curriculum (cycle, component)
Profile (elective component)
Teaching methods Interactive, case study, student-centered learning, problematic discussion Workload (incl. contact hours, self-study
hours)
Total workload: 180 hours.
Lectures: 30 hours, practical: 30 hours, independent work of students: 120 hours.
Credit points (total by discipline) 6 ECTS Required and recommended
prerequisites for joining the module Module objectives/intended learning outcomes
The purpose of the discipline is to familiarize undergraduates with the basic principles and methods of using the soft computing apparatus to solve various applied problems that arise in programming, as well as in the development and use of modern information technologies. Study the basic concepts of fuzzy set theory, the basics of fuzzy logic and fuzzy computing, build fuzzy models for applied problems, choose fuzzy modeling methods in relation to information technology.
Magistrates must know soft computing technologies focused on solving control problems with weakly structured control objects; be able to use soft computing tools - the technique of fuzzy systems (fuzzy sets, fuzzy logic, fuzzy controllers), artificial neural networks, genetic algorithms and
32 evolutionary modeling
Content Fuzzy decision-making methods. Method of hierarchical analysis.
Decision support system. Mathematical and software tools for decision support systems. Mathematical model of the rating model of product competitiveness.
Fuzzy systems. Models and methods of decision making with fuzzy information. linguistic variable. Fuzzy sets. Membership features. Basic definitions and operations on fuzzy sets. Basic operations and relations of fuzzy logic. Approximate output scheme, interpolation problem.
Mamdani and Sugeno fuzzy inference algorithms. Fuzzy databases.
Examination forms Written exam
Study and examination requirements The final mark will be weighted as follows:
-20 degrees for assignments and Class work;
-40 degrees for two intermediate controls;
-40 degrees for final Written Exam.
Two intermediate controls end with a colloquium (discussion of the course content). Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.
The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod. Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.
The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod
Technical, multimedia tools and software
e-Learning MOODLE, individual cards, White-board, Laptop, LCD Projector
Reading list 1. E.S. Volkova, V.B. Gisin, Fuzzy sets and soft computing in economics and finance, Moscow: KNORUS, 2019. 156 p.
2. Averkin A.N., et al. Fuzzy sets in artificial intelligence control models / ed. D.A.Pospelova - M.: Science. 2016. -312 p.
3. Liu B. Theory and practice of indefinite programming. –M.: BINOM.
Knowledge Lab. 2016.- 416 p.
4. Leonenkov A.V. Fuzzy modeling in the MATLAB environment and fuzzy TECH. - St. Petersburg: BHV-Petersburg. 2015. -736 p.
5. Wang X., Ruan D. and E. Kerre E.E. Mathematics of Fuzziness - Basic Issues. – Berlin-Heidelberg: Springer-Verlag. 2019. -219 p.
Module 24
Module code and name COMS 63014 Design and creation of artificial intelligence systems Semester(s), when the module is taught 3
Responsible for module person Kudubayeva S.A
Language of study Kazakh/Russian/English
Relationship with curriculum (cycle, component)
Profile (elective component)
Teaching methods Interactive, case study, student-centered learning, problematic discussion Workload (incl. contact hours, self-study
hours)
Total workload: 180 hours.
Lectures: 30 hours, practical: 30 hours, independent work of students: 120 hours.
Credit points (total by discipline) 6 ECTS
33 Required and recommended
prerequisites for joining the module Module objectives/intended learning outcomes
The discipline deals with the architecture of artificial intelligence systems, image recognition systems, issues of adaptation, training and self-learning of AI systems, perceptrons, methods and algorithms for analyzing the structure of multidimensional data, informal procedures, algorithmic models, the basics of REFAL and Prolog languages, key concepts of binary trees, basic concepts expert systems, automated synthesis, search for physical principles of action, methods for synthesizing human speech.
Students have knowledge in the field of artificial intelligence systems and decision making, studied software for building intelligent systems for various subject areas.
Students are able to conduct experimental testing of the created system and develop recommendations for its use. Know the concepts and methods of creating AIS based on the theory of artificial intelligence using a semantic-frame model of knowledge representation; Have skills in logic programming and creation of expert systems.
Content Architecture and main components of AI systems. The brain as a biological computer. Knowledge representation models. Intelligent systems and means of protection. Software agent and multi-agent system.
Multi-agent IS architecture. Stages of IS development. Pattern recognition systems (identification). Methods and algorithms for analyzing the structure of multidimensional data. A logical approach to building AI systems. Expert systems
Examination forms Written exam
Study and examination requirements The final mark will be weighted as follows:
-20 degrees for assignments and Class work;
-40 degrees for two intermediate controls;
-40 degrees for final Written Exam.
Two intermediate controls end with a colloquium (discussion of the course content). Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.
The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod. Final written examination (90 min.) have short answer questions, covering around half the marks, and then one long problem- solving practice task. On the written exam students are demonstrating their understanding of the course outline through the completion of tasks.
The next aspects of learning to program or an intellectual system development are assessed: the algorithms design, description of algorithms, the use of a programming environment to enter, edit, and debug cod
Technical, multimedia tools and software
e-Learning MOODLE, individual cards, White-board, Laptop, LCD Projector
Reading list 1. K. Naylor How to build your own expert system M. "Energoatomizdat"
2017.- 287 p.
2. Kukharenko B.G. Intelligent systems and technologies: textbook / B.G.
Kukharenko; - Moscow: Altair: MGAVT, 2015. - 115 p.
3.A.V. Timofeev Robots and artificial intelligence M. "Science" 2018- 192 p.
4. Laurier J.-L Systems of artificial intelligence M.: "Mir", 2015.—342 p.
with illustration
5. Makarenko S. I. Intelligent information systems: textbook. - Stavropol:
34 SF MGGU im. M. A. Sholokhova, 2019.– 206 p.: ill
Module 25