An Introduction to Materials Informatics The Elements of Machine Learning

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.

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.

Tongyi Zhang, born in October 1949 in Zhengzhou, China, is Expert in the interdisciplinary area of materials science and engineering and solid mechanics. He received his Ph.D. in 1985 in materials physics from the University of Science and Technology Beijing, China. He was Research Fellow of the Alexander von Humboldt Foundation, 1986-1988, Germany; Postdoctoral Fellow, 1988-1990, University of Rochester; and Associate Research Scientist, 1990-1993, Yale University. From 1993 to 2015, he worked at the Hong Kong University of Science and Technology, as Lecturer, Associate Professor, Professor, Chair Professor, and Fang Professor of Engineering.