Keyword: «machine learning»

The purpose of this article is to find the best model for forecasting time series, taking into account the minimization of errors and high accuracy of the forecast. The method of comparative analysis of the most popular "traditional" econometric models ARIMA and SARIMA is used. Algorithms and models are implemented in the Python programming environment with the connected libraries Sklearn, Pandas, Numpy, and Statsmodels. As input data sets, we imported data on product sales for 5 years in 10 stores. The results of the study confirm the superiority of the SARIMA model, in which the RMSE error is 20% less than when using ARIMA. It is concluded that to improve the quality of the time series forecast, it is preferable to use an algorithm based on the SARIMA model.

Modern problems require modern solutions. Personnel management is one of the key links in achieving the efficiency of the organization, so the demand for innovative solutions to the problems of personnel management, given the events of recent years, has grown and become the most significant.

The experience of developing the author's online course "Machine Learning", created on the Stepik platform, into the educational process of the university is considered. The requirements for choosing an educational platform for teaching programming are discussed. The key points concerning the selection of the content of training, the assessment of its quality, as well as its implementation in the educational process are noted. The instrumental application of the author's composition of libraries of the Python 3 programming language for organizing the practice of research in the field of machine learning is demonstrated, teaching tools are selected. First results of the experimental verification of the methodology on the basis of the Herzen State Pedagogical University of Russia are discussed.

This article presents the experience of introducing the study of machine learning algorithms in the school course of computer science and ICT. It describes some aspects of teaching machine learning and related areas, the peculiarities of schoolchildren's perception of project activities in this area and the prospects for the development of this area in the context of teaching schoolchildren and students.

With the continuous development of science and technology, especially after the pandemic, artificial intelligence technology has received wide attention and usage all over the world. In China's higher education system, English teaching in colleges and universities has always been an important component. The purpose of this research is to evaluate the usage of artificial intelligence technology in a university for teaching English in order to provide the recommendations for improving the quality of teaching and promotion the digitalization of the education. This research employs a combination of literature review, case study analysis and empirical research methods to summarize the advantages and status of the usage of artificial intelligence technology in the process of teaching English at the university by sorting and comparing the relevant literature. At the same time, combined with the real-life cases of teaching English at the Department of Finance of Inner Mongolia University (China), an in-depth analysis of the effect of applying artificial intelligence technology in teaching English was carried out. We found that intelligent teaching platforms can provide the students with personalized learning resources and learning services, which greatly improve students' learning efficiency; the application of natural language processing (NLP) technology in the teaching English in the universities can improve teaching efficiency, and promote the digitalization of the education process. Machine learning technologies can provide personalized learning resources and learning plans based on students' learning preferences and characteristics, greatly increasing their interest in learning English. The research’s results show that artificial intelligence technology has important ways of usage and broad prospects in teaching English at the universities. It can also improve teaching efficiency, and promote the digitalization of the education process.