The Elements of Machine Learning
Buch, Englisch, 465 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 902 g
ISBN: 978-981-99-7991-2
Verlag: Springer
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.
Zielgruppe
Graduate
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Chapter 1 Introduction
Chapter 2 Linear Regression
Chapter 3 Linear Classification
Chapter 4 Support Vector Machine
Chapter 5 Decision Tree and K-Nearest-Neighbors (KNN)
Chapter 6 Ensemble Learning
Chapter 7 Bayesian Theorem and Expectation-Maximization (EM) Algorithm
Chapter 8 Symbolic Regression
Chapter 9 Neural Networks
Chapter 10 Hidden Markov Chains
Chapter 11 Data Preprocessing and Feature Selection
Chapter 12 Interpretative SHAP Value and Partial Dependence Plot
Appendix 1 Vector and Matrix
Appendix 2 Basic Statistics




