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This article presents the results of using machine learning methods to evaluate the investment activity of various Russian regions. The task was considered from two points of view: obtaining information about the class to which a particular region belongs, and forming a quantitative estimate of the investment activity of the regions. In the first case, the solution was obtained with the help of a neural network system implemented in the MatLab 2018b. In the second case, a hybrid system ANFIS was used, making it possible to generate a quantitative estimate of investment activity.

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.

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The instruments of artificial intelligence (AI) that can be used in management of high technology production and training students are considered. Specific differences and characteristics of high technology production (HTP) that set certain requirements for such production management are specified. Brief information from the AI methods that include artificial neural networks, fuzzy logic, genetic algorithms and their combinations are given. It is indicated that there is a relation between the level of training masters and the requirements of modern productions. The necessity to use techniques and methods of AI when training students to form their competencies, knowledge and skills that comply with the HTP is explained. The techniques of using AI instruments in the educational process focused on the practical importance of the tasks being solved in such disciplines as HR management, risk management, strategic management, etc. are shown.