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Systems with Self-organization and Self-education
Major: Computer Science (Design and programming of intelligent systems and devices)
Code of subject: 6.122.12.E.072
Credits: 5.00
Department: Computer-Aided Design
Lecturer: Ph.D., Associate Professor Irina Yurchak
Semester: 5 семестр
Mode of study: денна
Завдання: The study of an educational discipline involves the formation of competencies in students of education:
General competences:
• Ability to abstract thinking, analysis and synthesis.
• Ability to apply knowledge in practical situations.
• Knowledge and understanding of the subject area and understanding of professional activity.
• Ability to search, process and analyze information from various sources.
• Ability to generate new ideas (creativity).
• Ability to evaluate and ensure the quality of the work performed.
Professional competences:
• Ability to identify statistical regularities of non-deterministic phenomena, use methods of computational intelligence, in particular statistical, neural network and fuzzy data processing, methods of machine learning and genetic programming, etc.
• Ability to think logically, draw logical conclusions, use formal languages ??and models of algorithmic calculations, design, develop and analyze algorithms, evaluate their effectiveness and complexity, solvability and unsolvability of algorithmic problems for adequate modeling of subject areas and creation of software and information systems
• The ability to use modern methods of mathematical modeling of objects, processes and phenomena, to develop models and algorithms for the numerical solution of mathematical modeling problems, to take into account the errors of the approximate numerical solution of professional problems.
• Ability to apply theoretical and practical bases of modeling methodology and technology to study the characteristics and behavior of complex objects and systems, to conduct computational experiments with processing and analysis of results.
• Ability to design and develop software using various programming paradigms: generalized, object-oriented, functional, logical, with appropriate models, calculation methods and algorithms, data structures and control mechanisms.
• Ability to intellectually analyze data based on methods of computational intelligence, including large and poorly structured data, their operational processing and visualization of analysis results in the process of solving applied problems.
Learning outcomes: As a result of studying the academic discipline, the student must be able to demonstrate the following learning outcomes:
1. know the general principles of functioning of systems with self-organization and self-learning;
2. know the basic algorithms for machine and deep learning;
3. know how to implement intelligent systems of various levels of complexity and purpose;
4. be able to determine the main parameters that affect the quality of functioning of adaptive systems, monitor intermediate results, find and correct errors in work;
5. know how to manage the parameters of adaptive systems with self-organization and self-learning;
6. have practical skills in working with intelligent systems and services.
Required prior and related subjects: Applied programming
Object-oriented programming
Algorithmization and programming
Data mining
Methods and systems of artificial intelligence
IT project management
Summary of the subject: The discipline "Systems with self-organization and self-learning" provides a body of knowledge on the theoretical foundations of adaptive systems capable of self-organization and self-learning. Features of machine and deep learning are considered. The main categories of algorithms for machine learning are considered - classical and natural algorithms, swarm and collective intelligence, neural networks. Provides information on popular intelligent systems and services that demonstrate intellectual abilities: search engines, computer and machine vision, dialog systems and chatbots, machine translation systems. Possibilities of self-organization of blockchain technologies for solving a number of problems are analyzed: electronic currency, smart contracts, digital identification data management, guarantee documents, logistics.
Опис: Current state of systems with self-organization and self-learning.
Machine learning.
Classic algorithms for learning with a teacher.
Classical algorithms for learning without a teacher. Reinforcement learning.
Natural algorithms.
Swarm intelligence.
Collective intelligence..
Modern architectures of neural networks.
Search systems.
Machine and computer vision.
Natural language processing.
Dialogue systems and chatbots.
Machine translation.
Blockchain technology.
Assessment methods and criteria: Current control (45%): Results of laboratory work, complex work, examination
Final control (55% of testing): exam, interviews.
Критерії оцінювання результатів навчання: 1. Distribution of points subject to the completion of the study plan, completion of all control work and the calendar plan for the performance of laboratory work, otherwise, according to the results of the semester control, the student is considered not certified.
2. The maximum number of points for the assessment of current control (PC) of knowledge per semester is 45 points.
3. Examination control is conducted in written and oral form.
4. The maximum number of points for evaluating the examination control is 55 points.
5. The student also completes the exam before the committee in written and oral form, with the questions and answer scores recorded on the exam sheet.
6. Students are admitted to the exam provided they complete the curriculum (including all laboratory work and calculation work).
Порядок та критерії виставляння балів та оцінок: 1. Distribution of points subject to the completion of the study plan, completion of all control work and the calendar plan for the performance of laboratory work, otherwise, according to the results of the semester control, the student is considered not certified.
2. The maximum number of points for the assessment of current control (PC) of knowledge per semester is 45 points.
3. Examination control is conducted in written and oral form.
4. The maximum number of points for evaluating the examination control is 55 points.
5. The student also completes the exam before the committee in written and oral form, with the questions and answer scores recorded on the exam sheet.
6. Students are admitted to the exam provided they complete the curriculum (including all laboratory work and calculation work).
Recommended books: 7. Recommended literature
1. Max Tegmark. Life 3.0. The age of artificial intelligence - Our format, 2019, 344 p..
2. Tariq Rashid. We create a neural network. - Dialectics-Williams, 2020, 272p.
3. Matt Harrison. Machine Learning: A Pocket Guide. - Dialectics-Williams, 2020, 320p.
4. George Gilder. Life after Google. The decline of big data and the emergence of the blockchain economy. - BookChef, 2021, 320 p.
5. Malcolm Frank, Paul Roehrig, Ben Pring. What to do when machines start doing everything. – Form, 2019, 320 p.
6. Don Tapscott, Alex Tapscott. Blockchain revolution. - Lytopis, 2019, 492p.
7. Sean Kennel, Benji Travis. YouTube secrets. - BookChef, 2020, 204 p..
8. Mark Randolph. NETFLIX. This idea will never work. - BookChef, 2019, 196 p.
9. Valentine's thrush. Digital currency in the world and in Ukraine. - Center for Educational Literature (CNL), 2022, 298p.
10. Marmanis H., Babenko D.. Algorithms of the intelligent Internet. Simbol, 2018, - 480 p.
8. Educational and methodological support
• Online collection of methodological instructions for the discipline (Lectures, methodological instructions for performing laboratory and independent work) (https://www.victoria.lviv.ua/library/students/sss/)
• Materials of the discipline in the VNS of the Lviv Polytechnic National University (https://vns.lpnu.ua/course/view.php?id=6437)
9. Information resources
1. Hybrid intellectual system [Electronic resource] // Wikipedia http://ru.wikipedia.org/wiki/Гыбридная_интелектульная_система.
2. Hybrid Intelligent System [Electronic resource]. –http://www.slideshare.net/ikensolutions/ hybrid-intelligent-systems-presentation
3. Google AI platform. – https://experiments.withgoogle.com/
4. Nvidia AI Playground - https://www.nvidia.com/en-us/research/ai-playground/
5. Satoshi Nakamoto "Bitcoin: A Peer-to-Peer Electronic Cash System" - https://bitcoin.org/bitcoin.pdf
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