Materials Data Science: Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering
暫譯: 材料數據科學:材料科學與工程中的數據挖掘、機器學習及數據驅動預測入門

Sandfeld, Stefan

  • 出版商: Springer
  • 出版日期: 2024-05-09
  • 售價: $3,440
  • 貴賓價: 9.5$3,268
  • 語言: 英文
  • 頁數: 618
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031465644
  • ISBN-13: 9783031465642
  • 相關分類: Machine LearningData-miningData Science
  • 海外代購書籍(需單獨結帳)

相關主題

商品描述

This text covers all of the data science, machine learning, and deep learning topics relevant to materials science and engineering, accompanied by numerous examples and applications. Almost all methods and algorithms introduced are implemented "from scratch" using Python and NumPy.

The book starts with an introduction to statistics and probabilities, explaining important concepts such as random variables and probability distributions, Bayes' theorem and correlations, sampling techniques, and exploratory data analysis, and puts them in the context of materials science and engineering. Therefore, it serves as a valuable primer for both undergraduate and graduate students, as well as a review for research scientists and practicing engineers.

The second part provides an in-depth introduction of (statistical) machine learning. It begins with outlining fundamental concepts and proceeds to explore a variety of supervised learning techniques for regression and classification, including advanced methods such as kernel regression and support vector machines. The section on unsupervised learning emphasizes principal component analysis, and also covers manifold learning (t-SNE and UMAP) and clustering techniques. Additionally, feature engineering, feature importance, and cross-validation are introduced.

The final part on neural networks and deep learning aims to promote an understanding of these methods and dispel misconceptions that they are a "black box". The complexity gradually increases until fully connected networks can be implemented. Advanced techniques and network architectures, including GANs, are implemented "from scratch" using Python and NumPy, which facilitates a comprehensive understanding of all the details and enables the user to conduct their own experiments in Deep Learning.


商品描述(中文翻譯)

這段文字涵蓋了與材料科學和工程相關的所有數據科學、機器學習和深度學習主題,並附有眾多範例和應用。幾乎所有介紹的方法和演算法都是使用 Python 和 NumPy 從頭開始實作的。

本書首先介紹統計學和機率,解釋重要概念,如隨機變數和機率分佈、貝葉斯定理和相關性、抽樣技術以及探索性數據分析,並將其置於材料科學和工程的背景中。因此,它對於本科生和研究生來說都是一本寶貴的入門書籍,同時也適合作為研究科學家和實務工程師的複習資料。

第二部分深入介紹(統計)機器學習。它首先概述基本概念,然後探討各種監督式學習技術,包括回歸和分類,並涵蓋如核回歸和支持向量機等先進方法。無監督學習的部分強調主成分分析,並涵蓋流形學習(t-SNE 和 UMAP)及聚類技術。此外,還介紹了特徵工程、特徵重要性和交叉驗證。

最後一部分關於神經網絡和深度學習,旨在促進對這些方法的理解,並消除它們是「黑箱」的誤解。複雜性逐漸增加,直到可以實作全連接網絡。先進技術和網絡架構,包括生成對抗網絡(GANs),都是使用 Python 和 NumPy 從頭開始實作的,這有助於全面理解所有細節,並使使用者能夠在深度學習中進行自己的實驗。

作者簡介

Prof. Dr. Stefan Sandfeld is Director of the Institute for Advanced Simulation: Materials Data Science and Informatics (IAS-9) Forschungszentrum Juelich, Germany; and Professor/Chair of Materials Data Science and Materials Informatics, RWTH Aachen University.​




作者簡介(中文翻譯)

斯特凡·桑德費爾德教授(Prof. Dr. Stefan Sandfeld)是德國尤利希研究中心(Forschungszentrum Juelich)高級模擬研究所:材料數據科學與資訊學(Institute for Advanced Simulation: Materials Data Science and Informatics, IAS-9)的主任;同時也是亞琛工業大學(RWTH Aachen University)材料數據科學與材料資訊學的教授及系主任。