Machine Learning

Major: Computer Science
Code of subject: 6.122.04.E.178
Credits: 4.00
Department: Artificial Intelligence Systems
Lecturer: Kaminsky R.M.
Semester: 6 семестр
Mode of study: денна
Learning outcomes: 1. Use the acquired knowledge to formulate machine learning tasks for computer decision-making systems. 2. Apply software packages to meet the learning objectives computer. 3. Use acquired knowledge for learning through evolutionary methods and algorithms. 4. To form knowledge and practical skills for use of basic methods of teaching computer to solve decision-making problems. 5. Provide a single methodological basis for interaction between the course Machine Learning and others subject disciplines. 6. Have an idea of ??the state and perspective of the development of mathematical methods and organization machine learning and their software. 7. Operationally apply the methods and methods of machine learning.
Required prior and related subjects: Systems and methods of artificial inteligence
Summary of the subject: General concepts of machine learning. Object classification. The concept of learning. Space of signs. Linear classifier and stochastic gradient. Self-organizing cards. The method of group accounting of arguments. Method of reference vectors
Assessment methods and criteria: 50- labs 50 - exam
Recommended books: 1. Кудін О.В. Моделювання систем та аналіз даних: методичні рекомендації до лабораторних робіт для студентів освітнього ступеня «бакалавр» напряму підготовки «Програмна інженерія» / О.В. Кудін. – Запоріжжя: ЗНУ, 2017. – 89 с. 2. Волошин, О. Ф. Моделі та методи прийняття рішень : навч. посіб. для студ. вищ. навч. закл. / О. Ф. Волошин, С. О. Мащенко. – 2-ге вид., перероб. та допов. – К. : Видавничо- поліграфічний центр "Київський університет", 2010. – 336 с. 3. Олдендерфер М. С. Факторный, дискриминантныи и кластерный анализ: Пер. с англ./Дж.-О. Ким, Ч. У. Мьюллер, У. Р. Клекка и др.; Под ред. И. С. Енюкова. – М.: Финансы и статистика, 1989. – 215 с: ил. 4. Довбиш А. С. Основи теорії розпізнавання образів : навч. посіб. : у 2 ч. / А. С. Довбиш, І. В. Шелехов. – Суми : Сумський державний університет, 2015. – Ч. 1. – 109 с. 5. Барковский С.С. Многомерный анализ данных методами прикладной статистики: Учебное пособие / С.С. Барковский, В.М. Захаров, А.М. Лукашов, А.Р. Нурутдинова, С.В. Шалагин – Казань: Изд. КГТУ, 2010. – 126 с. Табл. 5 . Ил. 105. Библиогр.: 12 наим.

Machine Learning (курсова робота)

Major: Computer Science
Code of subject: 6.122.04.E.179
Credits: 2.00
Department: Artificial Intelligence Systems
Lecturer: Boyko Nataliya
Semester: 6 семестр
Mode of study: денна
Learning outcomes: 1. Use the acquired knowledge to formulate machine learning tasks for computer decision-making systems. 2. Apply software packages to meet the learning objectives computer. 3. Use acquired knowledge for learning through evolutionary methods and algorithms. 4. To form knowledge and practical skills for use of basic methods of teaching computer to solve decision-making problems. 5. Provide a single methodological basis for interaction between the course Machine Learning and others subject disciplines. 6. Have an idea of ??the state and perspective of the development of mathematical methods and organization machine learning and their software. 7. Operationally apply the methods and methods of machine learning.
Required prior and related subjects: Systems and methods of artificial inteligence
Summary of the subject: General concepts of machine learning. Object classification. The concept of learning. Space of signs. Linear classifier and stochastic gradient. Self-organizing cards. The method of group accounting of arguments. Method of reference vectors
Assessment methods and criteria: 50 - writing a work 50- defense of course work
Recommended books: 1. Kononova K. Yu. Machine learning: methods and models: textbook for bachelors, masters and doctors of philosophy specialty 051 "Economics" / K. Yu. Kononova. – Kharkiv: V.N. Karazin KhNU, 2020. – 301 p. 2. Kurgaev O.P. Methods and systems of artificial intelligence / [Electronic resource]: a summary of lectures for students of the training direction 6.050101 "Computer science" full-time and part-time forms of study / O.P. Kurgaev - K.: NUHT, 2014. - 279 p. 3. Zgurovsky M.Z. Fundamentals of system analysis / M.Z. Zgurovskyi, N.D. Pankratova –K: BHV Publishing Group, 2007.-544p. 4. Methodological instructions for performing laboratory work in the discipline "Fuzzy models and methods of computational intelligence" for students of the specialty 8.05010102 ?Artificial intelligence systems: all forms of education / Compiled by: S.O. Saturday – Zaporizhzhia: ZNTU, 2015. – 50 p. 5. Kononyuk A.Yu. Neural networks and genetic algorithms - K.: "Korniichuk", 2008. - 446 p. 6. Mohylniy S. B. Machine learning using microcomputers: educational method. manual / edited by O. V. Lisovoy and others. - K., 2019. - 226 p. (http://man.gov.ua/files/49/Machine_Nav4ann_Mogilniy.pdf) 7. Shovba S.D. Machine learning: a starting course: an electronic study guide / Shtovba S.D., Kozachko O.M. – Vinnytsia: VNTU, 2020. – 81 p. (file:///C:/Users/Natas/Downloads/MachinelearningGettingStarted_CR.pdf) 8. Lyubun Z.M. Fundamentals of the theory of neural networks - Lviv, 2006. - 140 p.