材料信息学是一门新兴的交叉学科，为在材料基因组理念下加速材料科学研究和技术发展提供了一个全新的方法。作为材料和力学学者，作者在推动材料信息学发展方面做了大量工作，在人工智能（AI）、机器学习（ML）和材料科学技术融合交叉方面，有诸多的尝试和心得体会。作者旨在写一本易懂的材料信息学简介，以进一步推动材料信息学的发展。为便于读者尽快理解和掌握材料信息学的核心内容，兼顾成书的完整性，本书分为上下两卷，上卷侧重于机器学习基础，下卷侧重于深度学习并综述材料信息学的现状及发展前景。
本上卷共十二章，内容包括线性回归与线性分类、支持向量机、决策树和K近邻（KNN）、集成学习、贝叶斯定理和期望最大化（EM）算法、符号回归、神经网络、隐型马尔可夫链、数据预处理和特征选择、可解释性机器学习，等等。叙述力求从简单明了的数学定义和物理图像出发，密切结合材料科学研究案例，给出了各种算法的详细步骤，便于读者学习和运用。
本卷为机器学习基础的入门工具性读物，适合理工科科研人员和工程技术工作者快速且轻松地学习、理解和掌握材料信息学的AI和ML方法，也特别适合作为研究生和高年级本科生的教材。

Foreword
Preface
Symbols and Notations
Chapter 1 Introduction
References
Chapter 2 Linear Regression
2.1 Least Squares Linear Regression
2.2 Principal Component Analysis and Principal Component Regression
2.3 Least Absolute Shrinkage and Selection Operator (L1)
2.4 Ridge Regression (L2)
2.5 Elastic Net Regression
2.6 Multiply Task LASSO (MultiTaskLASSO)
Homework
References
Chapter 3 Linear Classification
3.1 Perceptron
3.2 Logistic Regression
3.3 Linear Discriminant Analysis
Homework
References
Chapter 4 Support Vector Machine
4.1 SVC
4.2 Kernel Functions
4.3 Soft Margin
4.4 SVR
Homework
References
Chapter 5 Decision Tree and K-Nearest-Neighbors (KNN)
5.1 Classification Trees
5.2 Regression Tree
5.3 K-Nearest-Neighbors (KNN) Methods
Homework
References
Chapter 6 Ensemble Learning
6.1 Boosting
6.1.1 AdaBoost
6.1.2 Gradient Boosting Machine (GBM)
6.1.3 eXtreme Gradient Boosting (XGBoost)
6.2 Bagging
Homework
References
Chapter 7 Bayesian Theorem and Expectation-Maximization (EM) Algorithm
7.1 Bayesian Theorem
7.2 Naive Bayes Classifier
7.3 Maximum Likelihood Estimation
7.3.1 Gaussian distribution
7.3.2 Weibull distribution
7.4 Bayesian Linear Regression
7.5 Expectation-Maximization (EM) Algorithm
7.5.1 Gaussian mixture model (GMM)
7.5.2 The mixture of Lorentz and Gaussian distributions
7.6 Gaussian Process (GP) Regression
Homework
References
Chapter 8 Symbolic Regression
8.1 Overview of Evolutionary Computation
8.2 Genetic Programming
8.3 Grammar-Guided Genetic Programming and Grammatical Evolution
8.4 The Application of LASSO in Symbolic Regression
Homework
References
Chapter 9 Neural Networks
9.1 Neural Networks and Perceptron
9.2 Back Propagation Algorithm
9.3 Regularization in NNs
9.3.1 L1 regularization
9.3.2 L2 regularization
9.4 Classification NNs
9.4.1 Binary classification
9.4.2 Multiclassification of multiply grades in a category
9.5 Autoencoders
9.5.1 Introduction
9.5.2 Denoising autoencoder
9.5.3 Sparse autoencoder
9.5.4 Variational autoencoder
Homework
References
Chapter 10 Hidden Markov Chains
10.1 Markov Chain
10.2 Stationary Markov Chain
10.3 Markov Chain Monte Carlo Methods
10.3.1 Metropolis Hastings (M-H) algorithm
10.3.2 Gibbs sampling algorithm
10.4 Calculation Methods for the Probability of Observation Sequence
10.4.1 Direct method
10.4.2 Forward method
10.4.3 Backward method
10.5 Estimation of Optimal State Sequence
10.5.1 Direct method
10.5.2 Viterbi algorithm
10.6 Estimation of Intrinsic Parameters—The Baum-Welch Algorithm
Homework
References
Chapter 11 Data Preprocessing and Feature Selection
11.1 Reliable Data, Normals and Anomalies
11.1.1 Local outlier factor
11.1.2 Isolated forest
11.1.3 One-class support vector machine
11.1.4 Support vector data description
11.2 Feature Selection
11.2.1 Filter approach
11.2.2 Wrapper approach
11.2.3 Embedded approach
Homework
References
Chapter 12 Interpretative SHAP Value and Partial Dependence Plot
12.1 SHapley Additive exPlanation value
12.2 The joint SHAP value of two features
12.3 Partial Dependence Plot
Homework
References
Appendix 1 Vector and Matrix
A1.1 Definition
A1.1.1 Vector
A1.1.2 Matrix
A1.2 Matrix Algebra
A1.2.1 Inverse and transpose
A1.2.2 Trace
A1.2.3 Determinant
A1.2.4 Eigenvalues and eigenvectors
A1.2.5 Singular value decomposition (SVD)
A1.2.6 Pseudo inverse
A1.2.7 Some useful identities
A1.3 Matrix Analysis
A1.3.1 Derivative of matrix
A1.3.2 Derivative of the determinant of a matrix
A1.3.3 Derivative of an inverse matrix
A1.3.4 Ja