An Introduction to Materials Informatics - Zhang, Tongyi; - Prospero Internet Bookshop

An Introduction to Materials Informatics: The Elements of Machine Learning
 
Product details:

ISBN13:9789819979912
ISBN10:9819979919
Binding:Hardback
No. of pages:479 pages
Size:235x155 mm
Language:English
Illustrations: 11 Illustrations, black & white; 126 Illustrations, color
700
Category:

An Introduction to Materials Informatics

The Elements of Machine Learning
 
Edition number: 1st ed. 2024
Publisher: Springer
Date of Publication:
Number of Volumes: 1 pieces, Book
 
Normal price:

Publisher's listprice:
EUR 106.99
Estimated price in HUF:
45 385 HUF (43 223 HUF + 5% VAT)
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Availability:

Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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  Piece(s)

 
Short description:

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.

Long description:

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.

Table of Contents:

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.