[正版]材料信息学导论.上机器学习基础 张著材料基因工程丛书

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材料信息学导论.上，机器学习基础
	ISBN	9787030728982
	定价	180
	作者	张统一 著; 编; 译;
	开本	16
	装帧	平装
	页数	457
	出版时间	2022年09月
	出版社	科学出版社

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。

Contents  
Foreword  
Preface  
Symbols and Notations  
Chapter 1 Introduction 1  
References 13  
Chapter 2 Linear Regression 15  
2.1 Least Squares Linear Regression 15  
2.2 Principal Component Analysis and Principal Component Regression 26  
2.3 Least Absolute Shrinkage and Selection Operator (L1) 37  
2.4 Ridge Regression (L2) 40  
2.5 Elastic Net Regression 44  
2.6 Multiply Task LASSO (MultiTaskLASSO) 49  
Homework 52  
References 53  
Chapter 3 Linear Classification 55  
3.1 Perceptron 57  
3.2 Logistic Regression 60  
3.3 Linear Discriminant Analysis 73  
Homework 80  
References 82  
Chapter 4 Support Vector Machine 83  
4.1 SVC 83  
4.2 Kernel Functions 88  
4.3 Soft Margin 96  
4.4 SVR 102  
Homework 108  
References 110  
Chapter 5 Decision Tree and K-Nearest-Neighbors (KNN) 112  
5.1 Classification Trees 112  
5.2 Regression Tree 121  
5.3 K-Nearest-Neighbors (KNN) Methods 129  
Homework 133  
References 134  
Chapter 6 Ensemble Learning 136  
6.1 Boosting 137  
6.1.1 AdaBoost 137  
6.1.2 Gradient Boosting Machine (GBM) 145  
6.1.3 eXtreme Gradient Boosting (XGBoost) 151  
6.2 Bagging 153  
Homework 158  
References 159  
Chapter 7 Bayesian Theorem and Expectation-Maximization (EM) Algorithm 160  
7.1 Bayesian Theorem 160  
7.2 Naive Bayes Classifier 161  
7.3 Maximum Likelihood Estimation 168  
7.3.1 Gaussian distribution 168  
7.3.2 Weibull distribution 170  
7.4 Bayesian Linear Regression 175  
7.5 Expectation-Maximization (EM) Algorithm 184  
7.5.1 Gaussian mixture model (GMM) 185  
7.5.2 The mixture of Lorentz and Gaussian distributions 197  
7.6 Gaussian Process (GP) Regression 209  
Homework 219  
References 219  
Chapter 8 Symbolic Regression 221  
8.1 Overview of Evolutionary Computation 221  
8.2 Genetic Programming 223  
8.3 Grammar-Guided Genetic Programming and Grammatical Evolution 225  
8.4 The Application of LASSO in Symbolic Regression 234  
Homework 235  
References 235  
Chapter 9 Neural Networks 238  
9.1 Neural Networks and Perceptron 238  
9.2 Back Propagation Algorithm 241  
9.3 Regularization in NNs 250  
9.3.1 L1 regularization 250  
9.3.2 L2 regularization 257  
9.4 Classification NNs 261  
9.4.1 Binary classification 261  
9.4.2 Multiclassification of multiply grades in a category 267  
9.5 Autoencoders 272  
9.5.1 Introduction 272  
9.5.2 Denoising autoencoder 273  
9.5.3 Sparse autoencoder 280  
9.5.4 Variational autoencoder 288  
Homework 311  
References 312  
Chapter 10 Hidden Markov Chains 313  
10.1 Markov Chain 313  
10.2 Stationary Markov Chain 317  
10.3 Markov Chain Monte Carlo Methods 318  
10.3.1 Metropolis Hastings (M-H) algorithm 320  
10.3.2 Gibbs sampling algorithm 321  
10.4 Calculation Methods for the Probability of Observation Sequence 325  
10.4.1 Direct method 325  
10.4.2 Forward method 328  
10.4.3 Backward method 330  
10.5 Estimation of Optimal State Sequence 332  
10.5.1 Direct method 332  
10.5.2 Viterbi algorithm 333  
10.6 Estimation of Intrinsic Parameters—The Baum-Welch Algorithm 334  
Homework 344  
References 345  
Chapter 11 Data Preprocessing and Feature Selection 347  
11.1 Reliable Data, Normals and Anomalies 348  
11.1.1 Local outlier factor 348  
11.1.2 Isolated forest 352  
11.1.3 One-class support vector machine 355  
11.1.4 Support vector data description 361  
11.2 Feature Selection 365  
11.2.1 Filter approach 366  
11.2.2 Wrapper approach 394  
11.2.3 Embedded approach 402  
Homework 408  
References 408  
Chapter 12 Interpretative SHAP Value and Partial Dependence Plot 410  
12.1 SHapley Additive exPlanation value 410  
12.2 The joint SHAP value of two features 426  
12.3 Partial Dependence Plot 427  
Homework 440  
References 440  
Appendix 1 Vector and Matrix 442  
A1.1 Definition 442  
A1.1.1 Vector 442  
A1.1.2 Matrix 442  
A1.2 Matrix Algebra 442  
A1.2.1 Inverse and transpose 442  
A1.2.2 Trace 443  
A1.2.3 Determinant 443  
A1.2.4 Eigenvalues and eigenvectors 444  
A1.2.5 Singular value decomposition (SVD) 444  
A1.2.6 Pseudo inverse 445  
A1.2.7 Some useful identities 445  
A1.3 Matrix Analysis 446  
A1.3.1 Derivative of matrix 446  
A1.3.2 Derivative of the determinant of a matrix 446  
A1.3.3 Derivative of an inverse matrix 447  
A1.3.4 Jacobian matrix and Hessian matrix 447  
A1.3.5 The chain rule 447  
References 447  
Appendix 2 Basic Statistics 448  
A2.1 Probability 448  
A2.1.1 Joint probability 448  
A2.1.2 Bayesian theorem and conjugation 448  
A2.1.3 Probability density of continuous variables 449  
A2.1.4 Quantile function 449  
A2.1.5 Expectation, variance and covariance of random variables 449  
A2.2 Distributions 449  
A2.2.1 Bernoulli distribution 450  
A2.2.2 Binomial distribution 450  
A2.2.3 Poisson distribution 450  
A2.2.4 Gaussian distribution 450  
A2.2.5 Weibull distribution 451  
A2.2.6 The chi-square (χ2) distribution and χ2-test 451  
A2.2.7 The student’s t-distribution and t-test 452  
References 452  
Index 453