ࡱ> $&!"#W bjbjJJD (a(a -&nn)))8a )`"L-#"O#O#O#*$*$*$suuuuuu$BJ*$*$*$*$*$O#O#...*$&O#O#s.*$s..gO#Ğd3P)R\_00`C$B)BggBD *$*$.*$*$*$*$*$2.*$*$*$`*$*$*$*$B*$*$*$*$*$*$*$*$*$nm : NATIONAL UNIVERSITY OF LIFE AND ENVIRONMENTAL SCIENCES OF UKRAINE Machines and equipment design department APPROVED Faculty of design and engineering 10 June 2025 CURRICULUM OF ACADEMIC DISCIPLINE ARTIFICIAL INTELLIGENCE SYSTEMS (title) Field of knowledge: G "Engineering, production and construction" Specialty: G11 "Mechanical Engineering (by specializations)" Academic programme "Machines and equipment of agricultural production" Orientation of the program: educational and scientific Faculty of design and engineering Developers: doctor of technical sciences, professor Romasevych Y.O., candidate of technical sciences, docent Krushelnytskyi V.V. Kyiv 2025 Description of the discipline Artificial intelligence systems (title) Studying the course AI Systems in Robotics is essential for understanding how artificial intelligence enhances robotic capabilities. AI enables robots to perceive environments, learn from data, make intelligent decisions, and adapt to changing conditionscrucial features in applications like autonomous navigation, object manipulation, and human-robot interaction. This course provides a foundation in machine learning, computer vision, natural language processing, and decision-making algorithms as applied to robotics. Mastery of AI systems empowers students to design smarter, more autonomous robots for industries such as healthcare, manufacturing, logistics, and exploration. With AI rapidly transforming machines, this knowledge is vital for innovating future technologies, ensuring efficiency, adaptability, and collaboration between humans and machines in real-world tasks. Area of knowledge, specialty, academic programme, academic degreeAcademic degree MasterSpecialty G11 Mechanical Engineering (by specializations)Academic programmeMachines and equipment of agricultural productionCharacteristics of the disciplineTypeSelectiveGeneral volume of hours120Number of credits ECTS 4Number of modules2Course project-Control formExamIndicators of the discipline for full-time forms of university studyYear of study1Term2Lectures16 h.Practical classes and seminars-Laboratory classes16 h.Self-study88 h.Number of hours per week for full-time students2 h. 1. Aim, competences and expected learning outcomes of the discipline Artificial intelligence systems (AIS) play an extremely important role in the development of robotics. They give robots the ability to adapt to new situations, learn from data, and make decisions, allowing them to become more efficient, flexible, and useful in a variety of fields, including agriculture. Here are a few aspects of the importance of artificial intelligence systems in the field of robotics: 1) autonomy and understanding of the environment (AIS help robots understand their environment by processing sensory data such as video, audio and touch sensors. This allows robots to learn and make decisions based on the collected data); 2 recognition of objects and planning of actions (AIS allows robots to recognize objects, people and other robots in their environment. The goal of the discipline is the formation of theoretical understanding and practical skills in the development of SSI and their application in the field of robotics. The tasks of the discipline consist in teaching: the main theoretical principles on which SSI is based, their application to the development of control systems for the movement of robots, planning their trajectory, processing sensory information, etc.; the use of software for the implementation of AIS in the field of robotics. Competences acquired: integral competence: the ability to solve complex tasks and problems of industrial mechanical engineering, which involve research and/or innovation and are characterized by uncertainty of conditions and requirements. general competences (GC): GC3. Ability to create new equipment and technologies in the field of mechanical engineering. GC 5. Ability to develop and implement plans and projects in the field of industrial mechanical engineering and related activities, to carry out relevant entrepreneurial activities. GC 6. Ability to design, research and use robotic systems and complexes to meet the needs of agricultural production. special (professional) competencies (SC): SC1. Ability to apply information and communication technologies. SC 3. Ability to search, process and analyze information from various sources. SC 6. Ability to generate new ideas (creativity). Expected learning outcomes (ELO): ELO02. Knowledge and understanding of mechanics and mechanical engineering and the prospects for their development. ELO4. Perform engineering calculations to solve complex tasks and practical problems in the field of mechanical engineering. Programme and structure of the discipline Modules and topicsNumber of hourstotalincludinglplabinds.stweeksModule 1. Basics of ANN architectures and their application in machinesTopic 1. Introduction. Basic concepts and applications of ANNs for machines234-4-151-4Topic 2. Mathematical basics of ANN operation192-2-155-6Topic 3. Special ANNs for machines182-2-147-8Total for module 1608-8-44-Module 2. Approaches to ANN trainingTopic 4. ANN training "with a teacher" and correspond machines problems234-4-159-12Topic 5. ANN training "with reinforcement" and correspond machines problems212-4-1513-15Topic 6. Backpropagation method162---1415Total for module 2608-8-44-Total hours120161688- 3. Topics of lectures !TopicHours1Introduction. Basic concepts and applications of ANNs for machines42Mathematical basics of ANN operation23Special ANNs for machines24ANN training "with a teacher" and correspond machines problems45ANN training "with reinforcement" and correspond machines problems26Backpropagation method2 4. Topic of laboratory (practical, seminars) classes !TopicHours1Preliminary data preparation22Creation of a classifier based on a perceptron23Multilayer perceptron24Study of recurrent ANNs26Classification indicators for assessing the quality of forecasts27Cross-validation to assess the quality of the classifier28Evaluation of the quality of models in classification tasks2 Topics of self-study !TopicHours1Analysis of areas of application of ANNs in robotics and agricultural production152Mathematical operations in ANNs153Recurrent ANNs144Preparation of data for training ANNs according to the  with teacher paradigm155Application of gradient-free optimization methods for training ANNs according to the reinforcement paradigm156Generalization of the method of backpropagation of the error (the case of multilayer ANNs)14 6. Methods of assessing expected learning outcomes: When teaching this discipline, the following diagnostic tools are used: oral interview; exam; module tests; defense of laboratory work. 7. Teaching methods: When teaching this discipline, the following methods are used: problem-based learning method; practice-oriented learning method; research-based learning method; educational discussions and debates method; teamwork and brainstorming method. Results assessment The knowledge of a higher education applicant is assessed on a 100-point scale, which is translated into a national assessment in accordance with the current "Regulations on Examinations and Tests at the Vlog of Ukraine." Distribution of points by types of educational activities Type of training activitiesResults teachingEvaluationModule 1. Basics of ANN architectures and their application in machinesLab 1ELO2, ELO4. To know the basics of ANN and their structures. To be able to prepare develop proper ANN structures for different robotics problems.10Lab 210Self-study work 110Lab 310Self-study work 210Lab 410Self-study work 310Module 1 test-30Overall on 1st module-100Module 2. Approaches to ANN trainingLab 5ELO2, ELO4. To know the basics of ANN training procedures. To be able to train ANN with different approaches.10Lab 610Self-study work 410Lab 710Lab 810Self-study work 510Self-study work 610Module 2 test-30Overall on 2nd module-100Class work-(1+2)/2*0,7 d"70Exam-30Overall for 2nd semester-(Class work+Exam)d"100 8.2 Scale for assessing students knowledge Students rating, pointsNational grading (exam/credits)90-100excellent74-89good60-73satisfactory0-59unsatisfactory 8.3 Assessment policy Deadlines and exam retaking rules works that are submitted late without valid reasons will be assessed with a lower grade. Module tests may be retaken with the permission of the lecturer if there are valid reasons (e.g. a sick leave). Academic integrity rulescheating during tests and exams is prohibited (including using mobile devices). Term papers and essays must have correct references to the literature usedAttendance rulesattendance is compulsory. For good reasons (e.g. illness, international internship), training can take place individually (online by the faculty deans consent) Teaching and learning aids Course on E-learn: https://elearn.nubip.edu.ua/course/view.php?id=5360 lecture notes and their presentations (in electronic form); methodological materials for studying the academic discipline for higher education students. 10. Recommended sources of information Laxmidhar Behera, Swagat Kumar, Prem Kumar Patchaikani, Ranjith Ravindranathan Nair, Samrat Dutta. Intelligent Control of Robotic Systems. 2020. 1st Edition. CRC Press. 674 p. Mellal M. A. (2022). Design and Control Advances in Robotics. IGI Global. 387 p. Zhang C., Wu J., Li C. (2024). Recent Progress in Robot Control Systems: Theory and Applications. MDPI. 312 p. Vladareanu L., Yu H., Wang H. Feng, Y. (2023). Advanced Intelligent Control in Robots. MDPI. 452 p. Gu J., Hu F., Zhou H., Fei Z., Yang E. (2024). Robotics and Autonomous Systems and Engineering Applications of Computational Intelligence. Springer. 434 p. 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