书目名称 | Machine Learning | 副标题 | The Basics | 编辑 | Alexander Jung | 视频video | | 概述 | Proposes a simple three-component approach to formalizing machine learning problems and methods.Interprets typical machine learning methods using the unified scientific cycle model: forming hypothesis | 丛书名称 | Machine Learning: Foundations, Methodologies, and Applications | 图书封面 |  | 描述 | Machine learning (ML) has become a commonplace element in our everyday lives and a standard tool for many fields of science and engineering. To make optimal use of ML, it is essential to understand its underlying principles. .This book approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. .The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods..The book’s three-component approach to ML provides uniform coverage of a wide range of concepts and techniques. As a case in point, techniques for regularization, privacy-preservation as well as explainability amount tospecific design choices for the model, data, and loss of a ML method. . | 出版日期 | Textbook 2022 | 关键词 | Machine Learning; Modelling; Artificial Intelligence; Deep Learning; Optimization; Data Analysis; Signal P | 版次 | 1 | doi | https://doi.org/10.1007/978-981-16-8193-6 | isbn_softcover | 978-981-16-8195-0 | isbn_ebook | 978-981-16-8193-6Series ISSN 2730-9908 Series E-ISSN 2730-9916 | issn_series | 2730-9908 | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor |
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