Computer Training Methods and Tools

Major: Computer Sciences
Code of subject: 7.122.03.O.56
Credits: 5.00
Department: Computer-Aided Design
Lecturer: Associate Professor at CAD department, PhD, Nazariy Andrushchak
Semester: 1 семестр
Mode of study: денна
Learning outcomes: The ability to create and use in practice algorithms of machine learning using the Python programming language, as well as to know how to analyze and present the results of such calculations and simulations. Mastering modern data analysis algorithms and improving the skills to work independently (in a project) or in a group (laboratory work, including leadership skills when executed), ability to obtain a result within a limited time with the emphasis on professional integrity and the impossibility of plagiarism.
Required prior and related subjects: Пререквізити: Системне програмування, Методи і засоби комп’ютерних інформаційних технологій Кореквізити: Системи штучного інтелекту, Розпізнавання образів і комп’ютерний зір Prerequisites: System programming, Methods and tools of computer information technology Corequisites: Artificial Intelligence Systems, Pattern Recognition and Computer Vision
Summary of the subject: The course "Methods and means of machine learning" is developed for master degree students of Lviv Polytechnic National University. The basis of the course is to master the theoretical foundations of methods and tools used in machine learning, develop students' skills for a better understanding of the basic ideas underlying these methods and tools, to teach students how to work with software that implements algorithms of machine learning. The main programming language for implementing machine learning algorithms in this course is Python. At the end of the course, a student will know: the main methods and algorithms of machine learning, methods of minimizing the average risk of machine learning, the peculiarities of supervised/unsupervised machine learning, evaluate the quality of machine learning, to solve the applied problems associated with the application of machine learning algorithms.
Assessment methods and criteria: Methods of knowledge diagnosing 1. Written exam. 2. Laboratory works. 3. Presentation and defense of the project. Criteria for evaluation Total for discipline - 100 points, among them: Laboratory works (5) - 25 points Control work (1) - 20 points Exam (1) - 55 points
Recommended books: 1. Andrushchak N.A. Methods and tools of machine learning: lecture notes for students of the second (master's) level of higher education of the Institute of Computer Science and Information Technology. - Lviv: Lviv Polytechnic National University Publishing House, 2018. - 224 p. (in Ukrainian) 2. Andrushchak N.A. Methods and tools of machine learning: laboratory workshop for students of the second (master's) level of higher education at the Institute of Computer Science and Information Technology. - Lviv: Lviv Polytechnic National University Publishing House, 2018. - 125 p. (in Ukrainian) 3. Mitchell T. Machine learning / T. Mitchell. – McGraw Hill, 1997. 4. Hastie T. The elements of statistical learning / T. Hastie, R. Tibshirani, J. Friedman. – Springer, 2001. 5. Ripley B. D. Pattern recognition and neural networks / B. D. Ripley. – Cambridge University Press, 1996. 6. Bishop C. M. Pattern recognition and machine learning / C. M. Bishop. – Springer, 2006. 7. Duda R. O. Pattern classification / R. O. Duda, P. E. Hart, D. G. Stork. – New York : JohnWiley and Sons, 2001.