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An introduction to materials informaticsthe elements of machine learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	An introduction to materials informaticsby Tongyi Zhang.
Reminder of title:	the elements of machine learning /
Author:	Zhang, Tongyi.
Published:	Singapore :Springer Nature Singapore :2025.
Description:	xvi, 479 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Materials.
Online resource:	https://doi.org/10.1007/978-981-99-7992-9
ISBN:	9789819979929$q(electronic bk.)

An introduction to materials informaticsthe elements of machine learning /
Zhang, Tongyi.

An introduction to materials informaticsthe elements of machine learning /[electronic resource] :by Tongyi Zhang. - Singapore :Springer Nature Singapore :2025. - xvi, 479 p. :ill., digital ;24 cm.

Introduction -- Linear Regression -- Linear Classification -- Support Vector Machine -- Decision Tree and K-Nearest-Neighbors (KNN) -- Ensemble Learning -- Bayesian Theorem and Expectation-Maximization (EM) Algorithm -- Symbolic Regression -- Neural Networks -- Hidden Markov Chains -- Data Preprocessing and Feature Selection -- Interpretative SHAP Value and Partial Dependence Plot.

This textbook educates current and future materials workers, engineers, and researchers on Materials Informatics. Volume I serves as an introduction, merging AI, ML, materials science, and engineering. It covers essential topics and algorithms in 11 chapters, including Linear Regression, Neural Networks, and more. Suitable for diverse fields like materials science, physics, and chemistry, it enables quick and easy learning of Materials Informatics for readers without prior AI and ML knowledge.

ISBN: 9789819979929$q(electronic bk.)

Standard No.: 10.1007/978-981-99-7992-9doiSubjects--Topical Terms:

203115
Materials.

LC Class. No.: QC415

Dewey Class. No.: 620.110285631

An introduction to materials informaticsthe elements of machine learning /
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300 $a xvi, 479 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Linear Regression -- Linear Classification -- Support Vector Machine -- Decision Tree and K-Nearest-Neighbors (KNN) -- Ensemble Learning -- Bayesian Theorem and Expectation-Maximization (EM) Algorithm -- Symbolic Regression -- Neural Networks -- Hidden Markov Chains -- Data Preprocessing and Feature Selection -- Interpretative SHAP Value and Partial Dependence Plot.
520 $a This textbook educates current and future materials workers, engineers, and researchers on Materials Informatics. Volume I serves as an introduction, merging AI, ML, materials science, and engineering. It covers essential topics and algorithms in 11 chapters, including Linear Regression, Neural Networks, and more. Suitable for diverse fields like materials science, physics, and chemistry, it enables quick and easy learning of Materials Informatics for readers without prior AI and ML knowledge.
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