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Machine Learning (курсова робота)
Major: Computer Sciences
Code of Subject: 6.122.04.E.158
Credits: 2
Department: Artificial Intelligence Systems
Lecturer: Boyko Nataliya
Semester: 4 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.
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
Recommended Books:
1. Kudin OV Systems modeling and data analysis: guidelines for
laboratory work for students of educational degree "bachelor" in the direction of training
"Software Engineering" / OV Kudin. - Zaporozhye: ZNU, 2017. - 89 p.
2. Voloshin, OF Models and methods of decision making: textbook. way. for students. higher textbook
lock / OF Voloshin, SO Mashchenko. - 2nd ed., Reworked. and add. - К.: Видавничо-
Polygraphic Center "Kyiv University", 2010. - 336 p.
3. Oldenderfer MS Factor, discriminant and cluster analysis: Per. with
eng./J.-O. Kim, C. W. Mueller, W. R. Klekka, etc .; Ed. IS Enyukova. - М .:
Finance and Statistics, 1989. - 215 p .: ill.
4. Dovbysh AS Fundamentals of the theory of pattern recognition: textbook. way. : у 2 ч. / А. С. Довбиш,
IV Shelekhov. - Sumy: Sumy State University, 2015. - Part 1. - 109 p.
5. Барковский С.С. Multidimensional data analysis by applied statistics methods:
Учебное пособие / С.С. Барковский, В.М. Zakharov, A.M. Lukashov, AR Nurutdinova,
S.V. Shalagin - Kazan: Ed. KSTU, 2010. - 126 p.
laboratory work for students of educational degree "bachelor" in the direction of training
"Software Engineering" / OV Kudin. - Zaporozhye: ZNU, 2017. - 89 p.
2. Voloshin, OF Models and methods of decision making: textbook. way. for students. higher textbook
lock / OF Voloshin, SO Mashchenko. - 2nd ed., Reworked. and add. - К.: Видавничо-
Polygraphic Center "Kyiv University", 2010. - 336 p.
3. Oldenderfer MS Factor, discriminant and cluster analysis: Per. with
eng./J.-O. Kim, C. W. Mueller, W. R. Klekka, etc .; Ed. IS Enyukova. - М .:
Finance and Statistics, 1989. - 215 p .: ill.
4. Dovbysh AS Fundamentals of the theory of pattern recognition: textbook. way. : у 2 ч. / А. С. Довбиш,
IV Shelekhov. - Sumy: Sumy State University, 2015. - Part 1. - 109 p.
5. Барковский С.С. Multidimensional data analysis by applied statistics methods:
Учебное пособие / С.С. Барковский, В.М. Zakharov, A.M. Lukashov, AR Nurutdinova,
S.V. Shalagin - Kazan: Ed. KSTU, 2010. - 126 p.
Assessment methods and criteria:
50 - writing a work
50- defense of course work
50- defense of course work
Machine Learning
Major: Computer Sciences
Code of Subject: 6.122.04.E.157
Credits: 4
Department: Artificial Intelligence Systems
Lecturer: Kaminsky R.M.
Semester: 4 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.
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
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.
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.
Assessment methods and criteria:
50- labs
50 - exam
50 - exam