Home/ Majors directory/Computer-based environmental and economic monitoring/Application Systems of Artificial Intelligence and Knowledge Engineering (Course work) (курсова робота)
Application Systems of Artificial Intelligence and Knowledge Engineering (Course work) (курсова робота)
Major: Computer-based environmental and economic monitoring
Code of subject: 7.122.08.E.019
Credits: 2.00
Department: Information Systems and Technologies
Lecturer: Ph.D.-M.Sc. Baran M.M., PhD Melnyk B.K.
Semester: 2 семестр
Mode of study: денна
Завдання: The study of an educational discipline involves the formation of education seekers
competencies:
general competences:
INT. The ability to solve problems of a research and/or innovative nature
in the field of computer science.
ZK01. Ability to abstract thinking, analysis and synthesis.
ZK02. Ability to apply knowledge in practical situations.
ZK03. Ability to communicate in the national language both orally and in writing.
ZK04. Ability to communicate in a foreign language.
ZK05. Ability to learn and master modern knowledge.
ZK07. Ability to generate new ideas (creativity).
professional competences:
SK01. Understanding the theoretical foundations of computer science.
SK02. The ability to formalize the subject area of ??a certain project in the form of
corresponding information model.
SK03. Ability to use mathematical methods to analyze formalized
subject area models.
SK04. Ability to collect and analyze data (including big data) to ensure
quality of project decision-making.
SK05. Ability to design, describe, analyze and optimize architectural
solutions of information and computer systems for various purposes.
SK08. Ability to develop and implement software creation projects
provision, including in unpredictable conditions, under unclear requirements and necessity
apply new strategic approaches, use software tools for
organization of teamwork on the project.
SK11. Ability to initiate, plan and implement development processes
information and computer systems and software, including its
development, analysis, testing, system integration, implementation and support.
FCS1.4. Mastering the skills of collective research work using
Internet technologies.
FCS2.1. Design skills of specialized information systems in the industry
environmental and economic monitoring.
FCS2.4. Ability to create intelligent decision support systems with
using environmental optimization methods.
FCS2.6. The ability to compile supporting documentation for creation and implementation
and operation of ecological and economic monitoring systems.
Learning outcomes: As a result of studying the academic discipline, the student must:
- have knowledge and understanding of the scientific principles of creating intellectual systems and knowledge bases of various complexity;
- be able to form theoretical and practical solutions for creating and filling a knowledge base based on Python;
- be able to use knowledge and skills when describing relations and restrictions using the logic of statements;
- be able to apply knowledge and practical skills in the process of data analysis;
- be able to practically apply knowledge in the process of creating interfaces to software based on JS/Python.
As a result of the study of the academic discipline, the student must be able to demonstrate the following learning outcomes:
PR1. Have specialized conceptual knowledge that includes current scientific achievements in the field of computer science and is the basis for original thinking and conducting research, critical thinking of problems in the field of computer science and at the border of fields of knowledge.
PR2. Have specialized computer science problem-solving skills necessary for conducting research and/or conducting innovative activities to develop new knowledge and procedures.
PR4. Manage work processes in the field of information technology, which are complex, unpredictable and require new strategic approaches
PR6. Develop a conceptual model of an information or computer system.
PR7. Develop and apply mathematical methods for the analysis of information models.
PR8. Develop mathematical models and data analysis methods (including large ones).
PR9. Develop algorithmic and software for data analysis (including large data).
PR11. Create new algorithms for solving problems in the field of computer science, evaluate their effectiveness and limitations on their application.
PR13. Assess and ensure the quality of information and computer systems for various purposes.
PR14. Test the software
PR15. Identify the needs of potential customers regarding the automation of information processing.
PR17. Identify and eliminate problematic situations during software operation, formulate tasks for its modification or reengineering.
PR18. Collect, formalize, systematize and analyze the needs and requirements for the information or computer system being developed, operated or supported
MIND. 1.1. Know the methods, methods and technologies of collecting information from various sources, content analysis of documents, data analysis and processing.
MIND. 1.3. Be able to mathematically formulate and investigate continuous and discrete mathematical models, justify the choice of methods and approaches for solving theoretical and applied problems in the field of computer science, analysis and interpretation.
UM 1.5. Develop models of data flows, storage and data spaces, knowledge bases for intelligent systems.
UM 1.6. Create big data analysis technologies based on the use of intelligent software components, artificial neural networks, machine learning, evolutionary modeling, genetic algorithms and fuzzy logic.
UM 1.7. Be able to intellectually analyze data based on computational intelligence methods, including large and poorly structured data, their operational processing and visualization of analysis results in the process of solving applied problems.
UM 2.1. Solve optimization problems in the design of monitoring systems, namely: mathematical models, optimality criteria, limitations; choose rational methods and algorithms for solving optimization and optimal control problems.
UM2.2. Demonstrate knowledge of the basic concepts of the theory of algorithms, formal models of algorithms, primitively recursive, general recursive and partially recursive functions, issues of computability, solvability and unsolvability of mass problems, concepts of time and space complexity of algorithms when solving computational problems.
AiB1. Ability to adapt to new conditions.
AiB2. Ability to make independent decisions in critical conditions.
AiB3. Ability to present work results.
KOM 1. Oral and written communication skills in Ukrainian;
oral and written communication skills in English.
KOM 2. Oral and written communication skills in English.
Required prior and related subjects: Theory of database and knowledge systems
Big data technologies in computer monitoring systems
Summary of the subject: The knowledge gained in the process of studying this discipline consists in revealing the mathematical features of the basic concepts of knowledge bases and the formation of recommendations for the use of various types of technical means in solving the problems of design, construction and increasing the efficiency of their use. The theoretical foundations of the definition of the concepts "knowledge base", "intellectual system" are explained and their categorical description is provided; classification of knowledge bases is considered and epistemological aspects of computer modeling are described; schemes of data analysis and knowledge extraction are investigated within the framework of the object-oriented approach and the principles of the Semantic Web.
Опис: Introduction. Applied aspects of the application of knowledge engineering for the construction of SSI.
Methods of extracting knowledge from data and texts. Theoretical aspects of knowledge extraction. Structuring methods. Evolution of knowledge acquisition systems. The role of the Python programming language in the development of modern intelligent systems.
Knowledge bases as a basis for the creation of SSI. Architecture. Classification.
Methods of acquiring knowledge. Field of knowledge. Field description language.
Data classification algorithms
Methods of presenting knowledge. Formal methods of presenting knowledge.
Logical models. The logic of statements. Logic of predicates. Semantic networks.
Data clustering algorithms
Methods of classification and systematization of knowledge. Theoretical aspects of knowledge structuring. Hierarchical approach.
Traditional structuring methodologies. Object-structural approach.
Methods of decision trees. ID3.CART.C4.5
Knowledge compilation methods. Communicative methods. Passive methods.
Active individual methods. Active group methods. Textological methods. Structuring methods.
Algorithms for finding associative rules
Latent structures of knowledge. Semantic spaces and grading.
Discovery of "hidden" knowledge structures. The method of repertory grids.
Practical aspects of implementing fuzzy logic algorithms
Parametric learning. Genetic algorithms. Bayesian networks.
Inductive learning. Decision trees. Associative rules.
Practical aspects of implementing fuzzy logic algorithms
Acquisition of knowledge by examples: climate control in water
Automation of industrial processes
Medical expert systems.
Assessment methods and criteria: Methods of assessing the level of achievement of the learner include:
1. Current control of the acquirer's work:
- test survey;
- individual oral survey at lectures;
- performance of individual work;
- performance of laboratory work.
2. Final (examination) control:
Completion of the examination control involves the performance of written and oral components.
The written component includes tasks of three difficulty levels:
- tasks of the 1st level - test tasks;
- tasks of the 2nd level - solving test problems;
- tasks of the 3rd level - solving practical problems.
Критерії оцінювання результатів навчання: -Current control (PC) - 40 points
Laboratory classes (performance of 1 class – 1 point)
Performing tests at the State Security Service -15 points.
Performance of individual work - 10 points
- Examination control - 60 points
written component 50 points
Порядок та критерії виставляння балів та оцінок: 100-88 points - certified with an “excellent” grade - High level: the student demonstrates an in-depth mastery of the conceptual and categorical apparatus of the discipline, systematic knowledge, skills and abilities of their practical application. The mastered knowledge, skills and abilities provide the ability to independently formulate goals and organize learning activities, search and find solutions in non-standard, atypical educational and professional situations. The applicant demonstrates the ability to make generalizations based on critical analysis of factual material, ideas, theories and concepts, to formulate conclusions based on them. His/her activity is based on interest and motivation for self-development, continuous professional development, independent research activities, implemented with the support and guidance of the teacher. 87-71 points - certified with a grade of “good” - Sufficient level: involves mastery of the conceptual and categorical apparatus of the discipline at an advanced level, conscious use of knowledge, skills and abilities to reveal the essence of the issue. Possession of a partially structured set of knowledge provides the ability to apply it in familiar educational and professional situations. Aware of the specifics of tasks and learning situations, the student demonstrates the ability to search for and choose their solution according to the given sample, to argue for the use of a particular method of solving the problem. Their activities are based on interest and motivation for self-development and continuous professional development. 70-50 points - certified with a grade of “satisfactory” - Satisfactory level: outlines the mastery of the conceptual and categorical apparatus of the discipline at the average level, partial awareness of educational and professional tasks, problems and situations, knowledge of ways to solve typical problems and tasks. The applicant demonstrates an average level of skills and abilities to apply knowledge in practice, and solving problems requires assistance, support from a model. The basis of learning activities is situational and heuristic, dominated by motives of duty, unconscious use of opportunities for self-development. 49-00 points - certified with a grade of “unsatisfactory” - Unsatisfactory level: indicates an elementary mastery of the conceptual and categorical apparatus of the discipline, a general understanding of the content of the educational material, partial use of knowledge, skills and abilities. The basis of learning activities is situational and pragmatic interest.
Recommended books: 1. Булгаков О.В., Зосімов В.В., Поздєєв В.О. Методи та системи штучного інтелекту: теорія і практика: навч. посіб. / Булгаков О.В., Зосімов В.В., Поздєєв В.О.– К. : Вид-во: Гельветика, 2020; укр. мова.
2. Єремєєв І.С., Гуйда О.Г. Інтелектуальні системи підготовки рішень: навч. посіб. / Єремєєв І.С., Гуйда О.Г. – К. : Вид-во: Гельветика, 2021; укр. мова.
3. Фратавчан В.Г., Фратавчан Т.М., Лукашів Т.О., Літвінчук Ю.А., Методи та системи штучного інтелекту: навч. посіб. / Чернівці: ЧНУ, 2023; укр. мова.
4. Коцовський В.М. Методи та системи штучного інтелекту / конс. лек. – Ужгород: ДВНЗ "Ужгородський національний університет", 2016; укр. мова
5. Харченко В. О. Х 22 Основи машинного навчання : навч. посiб. / В. О. Харченко. – Суми : Сумський державний унiверситет, 2023. – 264 с.
6. Burkov A. The hundred-page machine learning book / Canada : Quebec City, 2019 – 100 p.
7. Deisenroth M. P. Mathematics for machine learning / M. P. Deisenroth, A. A. Faisal, C. S. Ong. – New York : Cambridge University Press, 2020. – 412 p.
8. Шолле, Ф. Глибоке навчання з Python (2-е видання). Manning Publications. – Київ:Хайнінг. - 2021. 400 с.
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