Materials Data Science (Record no. 59704)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04411nam a22005775i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-46565-9 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20251030112031.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240508s2024 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783031465659 |
| -- | 978-3-031-46565-9 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-3-031-46565-9 |
| Source of number or code | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TA418.5-.84 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | TGMT |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | TEC021000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | TGMT |
| Source | thema |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Sandfeld, Stefan. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 9 (RLIN) | 76501 |
| 245 10 - TITLE STATEMENT | |
| Title | Materials Data Science |
| Medium | [electronic resource] : |
| Remainder of title | Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering / |
| Statement of responsibility, etc. | by Stefan Sandfeld. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2024. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Cham : |
| Name of producer, publisher, distributor, manufacturer | Springer International Publishing : |
| -- | Imprint: Springer, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | XXVI, 618 p. 200 illus. in color. |
| Other physical details | online resource. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS | |
| File type | text file |
| Encoding format | |
| Source | rda |
| 490 1# - SERIES STATEMENT | |
| Series statement | The Materials Research Society Series, |
| International Standard Serial Number | 2730-7379 |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. . |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| General subdivision | Analysis. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 9 (RLIN) | 6941 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 9 (RLIN) | 58441 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 9 (RLIN) | 66708 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 9 (RLIN) | 57783 |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 9 (RLIN) | 58441 |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 9 (RLIN) | 66709 |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer Nature eBook |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Printed edition: |
| International Standard Book Number | 9783031465642 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Printed edition: |
| International Standard Book Number | 9783031465666 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Printed edition: |
| International Standard Book Number | 9783031465673 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | The Materials Research Society Series, |
| International Standard Serial Number | 2730-7379 |
| 9 (RLIN) | 76502 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://doi.org/10.1007/978-3-031-46565-9">https://doi.org/10.1007/978-3-031-46565-9</a> |
| 912 ## - | |
| -- | ZDB-2-CMS |
| 912 ## - | |
| -- | ZDB-2-SXC |
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