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Materials data science = introduction to data mining, machine learning, and data-driven predictions for materials science and engineering /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Materials data science/ by Stefan Sandfeld.
Reminder of title:	introduction to data mining, machine learning, and data-driven predictions for materials science and engineering /
Author:	Sandfeld, Stefan.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xxvi, 618 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction to Data, Data Mining, and Prediction in Materials Science -- A Primer on Probabilities, Distributions and Statistics -- Introduction to Statistical Machine Learning -- Artificial Neural Networks and Deep Learning -- Advanced Topics of Machine Learning and Data Mining in Materials Science and Engineering -- Conclusion Outlook -- Appendix.
Contained By:	Springer Nature eBook
Subject:	Materials - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-46565-9
ISBN:	9783031465659

Materials data science = introduction to data mining, machine learning, and data-driven predictions for materials science and engineering /
Sandfeld, Stefan.

Materials data scienceintroduction to data mining, machine learning, and data-driven predictions for materials science and engineering /[electronic resource] :by Stefan Sandfeld. - Cham :Springer International Publishing :2024. - xxvi, 618 p. :ill., digital ;24 cm. - The materials research society series,2730-7379. - Materials research society series..

Introduction to Data, Data Mining, and Prediction in Materials Science -- A Primer on Probabilities, Distributions and Statistics -- Introduction to Statistical Machine Learning -- Artificial Neural Networks and Deep Learning -- Advanced Topics of Machine Learning and Data Mining in Materials Science and Engineering -- Conclusion Outlook -- Appendix.

This text covers all of the artificial intelligence, deep learning, and data science topics relevant to materials science and engineering, accompanied by numerous examples and applications. The book begins with a concise introduction to statistics and probabilities, explaining important concepts and definitions such as probability functions and distributions, sampling and data preparation, Bayes' theorem, and statistical significance testing in the context of materials science. As such it is a useful introduction for both undergraduate and graduate students as well as a refresher for research scientists and practicing engineers. The second part is a detailed description of (statistical) machine learning and deep learning. It considers a range of supervised and unsupervised methods including multi-output regression, random forests, time series prediction, and clustering as well as a number of different deep learning networks such as convolutional neural networks, auto-encoder, or generative adversarial networks. The degree of detail and theory is such that all methods can be understood and critically discussed, and it is reinforced by extensive examples within materials science and engineering. The final part considers six complex applications and advanced topics of machine learning and data mining in materials science and engineering. A comprehensive appendix is included, summarizing the most important statistical and mathematical techniques. 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 scientists and engineers; Maximizes intuitive understanding with materials science and physics examples, coding exercises, and online material.

ISBN: 9783031465659

Standard No.: 10.1007/978-3-031-46565-9doiSubjects--Topical Terms:

755339
Materials
--Data processing.

LC Class. No.: TA403

Dewey Class. No.: 620.11

Materials data science = introduction to data mining, machine learning, and data-driven predictions for materials science and engineering /
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245 1 0 $a Materials data science $h [electronic resource] : $b introduction to data mining, machine learning, and data-driven predictions for materials science and engineering / $c by Stefan Sandfeld.
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505 0 $a Introduction to Data, Data Mining, and Prediction in Materials Science -- A Primer on Probabilities, Distributions and Statistics -- Introduction to Statistical Machine Learning -- Artificial Neural Networks and Deep Learning -- Advanced Topics of Machine Learning and Data Mining in Materials Science and Engineering -- Conclusion Outlook -- Appendix.
520 $a This text covers all of the artificial intelligence, deep learning, and data science topics relevant to materials science and engineering, accompanied by numerous examples and applications. The book begins with a concise introduction to statistics and probabilities, explaining important concepts and definitions such as probability functions and distributions, sampling and data preparation, Bayes' theorem, and statistical significance testing in the context of materials science. As such it is a useful introduction for both undergraduate and graduate students as well as a refresher for research scientists and practicing engineers. The second part is a detailed description of (statistical) machine learning and deep learning. It considers a range of supervised and unsupervised methods including multi-output regression, random forests, time series prediction, and clustering as well as a number of different deep learning networks such as convolutional neural networks, auto-encoder, or generative adversarial networks. The degree of detail and theory is such that all methods can be understood and critically discussed, and it is reinforced by extensive examples within materials science and engineering. The final part considers six complex applications and advanced topics of machine learning and data mining in materials science and engineering. A comprehensive appendix is included, summarizing the most important statistical and mathematical techniques. 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 scientists and engineers; Maximizes intuitive understanding with materials science and physics examples, coding exercises, and online material.
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