Article
Article name Formation of Bachelor`s Skills in the Field of Machine Learning and Intelligent Data Analysis
Authors Lapenok M.V. Doctor of Pedagogy, Assistant Professor, rina_l@mail.ru
Shestakova L.G. Candidate of Pedagogy, Associate Professor, shestakowa@yandex.ru
Bibliographic description Lapenok M. V., Shestakova L. G. Formation of Bachelor`s Skills in the Field of Machine Learning and Intelligent Data Analysis // Scholarly Notes of Transbaikal State University. 2024. Vol. 19, no. 3. P. 17–26. DOI: 10.21209/2658-7114-2024-19-3-17-26.
Section THEORY AND METHODOLOGY OF PROFESSIONAL EDUCATION
UDK 372.862
DOI 10.21209/2658-7114-2024-19-3-17-26
Article type Original article
Annotation The article presents the content of the educational disciplines “Workshop on Python Programming” and “Fundamentals of Artificial Intelligence”, included in the module “Data Analysis. Machine learning. Artificial Intelligence”, implemented in Ural universities. Modern teachers note that when preparing the next gene­ration of teachers, the priority should be their acquisition of experience in using artificial intelligence tools in teaching. The objectives of mastering the module are developing students’ skills in the field of neural network mathematical modeling, developing knowledge about basic data analysis algorithms, development of skills in visualization and interpretation of data to solve applied problems using object-oriented programming technologies and artificial neural network technologies. As part of project activities, students gain experience in data mining by completing research projects (for example, a project on comparative assessment of the effectiveness of multilayer perceptron learning algorithms) and applied projects (for example, on the development of a neural network system for predicting the success of schoolchildren’s research work; on the development of a neural network system for predicting student attendance training sessions). At USPU, software systems have been developed in Python that implement the presented neural network prognostic systems. Educational and methodological materials for the disciplines of the module include video lectures and presentations for them, laboratory work (including those based on GoogleColab notebooks), datasets, test sets, theory questions and practical tasks for exams and tests. The effectiveness of the developed teaching methodology is confirmed by the results of a pedagogical experiment.
Key words neural network technologies, data mining, predictive systems, machine learning, content of the academic discipline
Article information
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