Overview
- 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
Part of the book series: The Materials Research Society Series (MRSS)
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Table of contents (19 chapters)
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Front Matter
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Introduction and Foundations
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Front Matter
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A Primer on Probabilities, Distributions, and Statistics
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Front Matter
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Keywords
- Data mining
- data science
- data-driven
- machine learning
- deep learning
- supervised learning
- unsupervised learning
- regression
- classification
- cross validation
- feature engineering
- convolutional neural network
- RNN
- summary statistics
- explorative data analysis
- data transformation
- outlier detection
- anomalie detection
About this book
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) and clustering techniques. Additionally, feature engineering, feature importance, and cross-validation are introduced.
The final part on neural networks and deep learning aims to promote an understanding of these methods and dispel misconceptions that they are a “black box”. The complexity gradually increases until fully connected networks can be implemented. Advanced techniques and network architectures, including GANs, are implemented “from scratch” using Python and NumPy, which facilitates a comprehensive understanding of all the details and enables the user to conduct their own experiments in Deep Learning.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Materials Data Science
Book Subtitle: Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering
Authors: Stefan Sandfeld
Series Title: The Materials Research Society Series
DOI: https://doi.org/10.1007/978-3-031-46565-9
Publisher: Springer Cham
eBook Packages: Chemistry and Materials Science, Chemistry and Material Science (R0)
Copyright Information: The Materials Research Society 2024
Hardcover ISBN: 978-3-031-46564-2Published: 09 May 2024
Softcover ISBN: 978-3-031-46567-3Due: 14 June 2024
eBook ISBN: 978-3-031-46565-9Published: 08 May 2024
Series ISSN: 2730-7360
Series E-ISSN: 2730-7379
Edition Number: 1
Number of Pages: XXVI, 618
Number of Illustrations: 200 illustrations in colour
Topics: Materials Science, general, Data Mining and Knowledge Discovery, Artificial Intelligence, Data Structures and Information Theory