Application of machine learning methods in teaching simulation of future chemistry teachers

- Семеріков, С.О. (orcid.org/0000-0003-0789-0272) (2018) Application of machine learning methods in teaching simulation of future chemistry teachers In: Технології навчання хімії у школі та ЗВО : збірник тез доповідей Всеукраїнської науково-практичної Інтернет-конференції , 1 . КДПУ, м. Кривий Ріг, Україна, pp. 10-19.

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Abstract

Starting from 2018-2019, the discipline "Numerical Methods and Modeling" was introduced for graduate students with the additional specialty "Informatics", aimed at forming the system of theoretical knowledge on the fundamentals of the numerical methods and practical skills of the students for the development and research of mathematical models. One of the main tasks of the discipline is to provide a set of knowledge necessary to understand the problems that arise during the construction and use of modern intelligent systems and to familiarize students with the basic principles of neural network modeling: - general characteristics of biological and artificial neurons; - Hebb's artificial neural network, classical and modified perceptron; - types of activation functions that have become distributed in artificial neural networks; - technology of designing single-layer and multi-layered artificial neural networks; - algorithms for learning neural networks. These issues have been addressed in the last decades within the framework of Machine Learning - a section of artificial intelligence that considers methods for constructing algorithms and programs based on them that can "learn" by presenting empirical data (precedents or observations) in which patterns are revealed, and on the basis of which models are being constructed, which enable to further predict certain characteristics for new objects. Unfortunately, the classical (and the most popular in the world) machine learning course of Andrew Ng, located on Coursera, focused primarily on the freshmens in computer science - it gives the opportunity to offer it for self-study, but does not solve the main problem: the provision of content models that reflect the specifics of the main specialty - chemistry.

Item Type:	Book Section
Keywords:	machine learning, modeling, future chemistry teachers
Subjects:	Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 00 Prolegomena. Fundamentals of knowledge and culture. Propaedeutics > 004 Computer science and technology. Computing. Data processing > 004.9 ІКТ ( Application-oriented computer-based techniques ) > 004.93 Pattern information processing Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 00 Prolegomena. Fundamentals of knowledge and culture. Propaedeutics > 004 Computer science and technology. Computing. Data processing > 004.9 ІКТ ( Application-oriented computer-based techniques ) > 004.94 Simulation Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 3 Social Sciences > 37 Education > 378 Higher education. Universities. Academic study Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 5 Мathematics. natural sciences > 54 Chemistry.
Divisions:	Institute for Digitalisation of Education > Department of the Cloud-Вased Systems of ICT in Education
Depositing User:	Сергій Олексійович Семеріков
Date Deposited:	04 Dec 2018 08:08
Last Modified:	31 Jan 2019 23:59
URI:	https://lib.iitta.gov.ua/id/eprint/712747

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