Methods of Machine Learning in the Master’s Educational Program

Saint Petersburg State University of Aerospace Instrumentation (SUAI), 67, Bolshaya Morskaya Str., Saint-Petersburg, 190000, Russia

^* Corresponding author: mkrichevsky@mail.ru

Abstract

This article considers methods of machine learning, which are introduced into the master’s educational program under the direction of “Organization and management of knowledge-intensive industries”. This direction should be primarily focused on the digitalization of education. Digital economy, which is rapidly becoming part of modern management and production methods, is changing approaches to education and universities, which should graduate people who comply with the requirements of the digital job environment. The study is aimed at demonstrating the capabilities of machine learning methods in case of their introduction into the disciplines of this direction. Thus, we can switch from a qualitative description of most disciplines in this direction to a quantitative interpretation of the results. The task at hand is best solved by such machine learning tools as neural and fuzzy systems that can be used to solve classification, regression and clustering problems. We have analysed the composition of disciplines and have chosen the most important ones in terms of the introduction of machine learning methods into them. The article presents the possibilities of using machine learning methods by the example of a number of practical exercises that are included in the programs of disciplines of this direction. We have identified a number of disciplines of this direction, which need to be supplemented with additional machine learning materials. The article offers the composition of such materials, including theoretical foundations and practical exercises in selected disciplines. The study provides solutions of the most important practical tasks from various disciplines, obtained with the help of Statistica and MatLab software products.