| 書目名稱 | Materials Data Science | | 副標題 | Introduction to Data | | 編輯 | Stefan Sandfeld | | 視頻video | http://file.papertrans.cn/626/625766/625766.mp4 | | 概述 | Introduces machine learning/deep learning methods in detail based on examples and data from materials science.Covers all theoretical foundations in an accessible manner, tailored to materials scientis | | 叢書名稱 | The Materials Research Society Series | | 圖書封面 |  | | 描述 | .This text covers all of the data science, machine learning, and deep learning topics relevant to materials science and engineering, accompanied by numerous examples and applications. Almost all methods and algorithms introduced? are implemented “from scratch” using Python and NumPy...The book starts with an introduction to statistics and probabilities, explaining important concepts such as random variables and probability distributions, Bayes’ theorem and correlations, sampling techniques, and exploratory data analysis, and puts them in the context of materials science and engineering. Therefore, it serves as a valuable primer for both undergraduate and graduate students, as well as a review for research scientists and practicing engineers. ..The second part provides an in-depth introduction of (statistical) machine learning. It begins with outlining fundamental concepts and proceeds to explore a variety of supervised learning techniques for regression and classification, including advanced methods such as kernel regression and support vector machines. The section on unsupervised learning emphasizes principal component analysis, and also covers manifold learning (t-SNE and UMAP) a | | 出版日期 | Textbook 2024 | | 關鍵詞 | Data mining; data science; data-driven; machine learning; deep learning; supervised learning; unsupervised | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-46565-9 | | isbn_softcover | 978-3-031-46567-3 | | isbn_ebook | 978-3-031-46565-9Series ISSN 2730-7360 Series E-ISSN 2730-7379 | | issn_series | 2730-7360 | | copyright | The Materials Research Society 2024 |
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