M. Gopal曾任印度理工学院德里分校教授，是一位优秀的作者、教师和研究员，还是一位享誉全球的院士。他是《控制工程》等5本书的作者或合著者，其著作被译为多国语言，在世界范围广泛使用。他在国外知名视频网站（YouTube）上的视频课程是印度理工学院最受欢迎的课程之一。Gopal教授是机器学习领域的知名研究者，著有150多篇研究论文，部分发表在高影响因子的期刊上。他目前的研究兴趣在机器学习和智能控制领域。
M. Gopal曾任印度理工学院德里分校教授，是一位优秀的作者、教师和研究员，还是一位享誉全球的院士。他是Control Engineering等5本书的作者或合著者，其著作被译为多国语言，在世界范围广泛使用。他在YouTube上的视频课程是印度理工学院最受欢迎的课程之一。Gopal教授是机器学习领域的知名研究者，著有150多篇研究论文，部分发表在高影响因子的期刊上。他目前的研究兴趣在机器学习和智能控制领域。
目錄：
目录
1. Introduction 引言 1
1.1 Towards Intelligent Machines 走向智能机器 1
1.2 Well-Posed Machine Learning Problems 适定的机器学习问题 5
1.3 Examples of Applications in Diverse Fields 不同领域的应用实例 7
1.4 Data Representation 数据表示 12
1.4.1 Time Series Forecasting 时间序列预测 15
1.4.2 Datasets for Toy Unreastically Simple and Realistic Problems
初级问题和现实问题数据集 17
1.5 Domain Knowledge for Productive use of Machine Learning
使机器学习有效应用的领域知识 18
1.6 Diversity of Data： StructuredUnstructured 数据多样性：结构化非结构化 20
1.7 Forms of Learning 学习形式 21
1.7.1 SupervisedDirected Learning 监督指导学习 21
1.7.2 UnsupervisedUndirected Learning 非监督无指导学习 22
1.7.3 Reinforcement Learning 强化学习 22
1.7.4 Learning Based on Natural Processes： Evolution, Swarming, and Immune Systems
基于自然处理的学习：进化、集群和免疫系统23
1.8 Machine Learning and Data Mining 机器学习和数据挖掘 25
1.9 Basic Linear Algebra in Machine Learning Techniques
机器学习技术中的基础线性代数 26
1.10 Relevant Resources for Machine Learning 机器学习的相关资源 34
2. Supervised Learning： Rationale and Basics 监督学习：基本原理和基础 36
2.1 Learning from Observations 从观测中学习 36
2.2 Bias and Variance 偏差和方差 42
2.3 Why Learning Works： Computational Learning Theory
学习为什么有效：计算学习理论 46
2.4 Occams Razor Principle and Overfitting Avoidance
奥卡姆剃刀原理和防止过拟合 49
2.5 Heuristic Search in Inductive Learning 归纳学习中的启发式搜索 51
2.5.1 Search through Hypothesis Space 假设空间搜索 52
2.5.2 Ensemble Learning 集成学习 53
2.5.3 Evaluation of a Learning System 学习系统的评价 55
2.6 Estimating Generalization Errors 估计泛化误差 56
2.6.1 Holdout Method and Random Subsampling 留出法和随机下采样 56
2.6.2 Cross-validation 交叉验证 57
2.6.3 Bootstrapping 自助法 58
2.7 Metrics for Assessing Regression Numeric Prediction Accuracy
评价回归（数值预测）精度的指标 59
2.7.1 Mean Square Error 均方误差 60
2.7.2 Mean Absolute Error 平均绝对误差 60
2.8 Metrics for Assessing Classification Pattern Recognition Accuracy
评价分类（模式识别）精度的指标 61
2.8.1 Misclassification Error 误分类误差 61
2.8.2 Confusion Matrix 混淆矩阵 62
2.8.3 Comparing Classifiers Based on ROC Curves 基于ROC曲线的分类器比较 66
2.9 An Overview of the Design Cycle and Issues in Machine Learning
机器学习中的设计周期和问题概述 68
3. Statistical Learning 统计学习 73
3.1 Machine Learning and Inferential Statistical Analysis 机器学习与推断统计分析 73
3.2 Descriptive Statistics in Learning Techniques 学习技术中的描述统计学 74
3.2.1 Representing Uncertainties in Data： Probability Distributions
表示数据中的不确定性：概率分布 75
3.2.2 Descriptive Measures of Probability Distributions 概率分布的描述方法 80
3.2.3 Descriptive Measures from Data Sample 数据样本的描述方法 83
3.2.4 Normal Distributions 正态分布 84
3.2.5 Data Similarity 数据相似性 85
3.3 Bayesian Reasoning： A Probabilistic Approach to Inference
贝叶斯推理：推断的概率方法 87
3.3.1 Bayes Theorem 贝叶斯定理 88
3.3.2 Naive Bayes Classifier 朴素贝叶斯分类器 93
3.3.3 Bayesian Belief Networks 贝叶斯信念网络 98
3.4 k-Nearest Neighbor k-NN Classifier k近邻（k-NN）分类器 102
3.5 Discriminant Functions and Regression Functions 判别函数和回归函数 106
3.5.1 Classification and Discriminant Functions 分类和判别函数 107
3.5.2 Numeric Prediction and Regression Functions 数值预测和回归函数 108
3.5.3 Practical Hypothesis Functions 实践应用中的假设函数 109
3.6 Linear Regression with Least Square Error Criterion
基于最小二乘误差准则的线性回归法 112
3.6.1 Minimal Sum-of-Error-Squares and the Pseudoinverse 最小误差平方和与伪逆 113
3.6.2 Gradient Descent Optimization Schemes 梯度下降法优化方案 115
3.6.3 Least Mean Square LMS Algorithm 最小均方（LMS）算法 115
3.7 Logistic Regression for Classification Tasks 分类任务的逻辑回归法 116
3.8 Fishers Linear Discriminant and Thresholding for Classification
Fisher线性判别和分类阈值120
3.8.1 Fishers Linear Discriminant Fisher线性判别式 120
3.8.2 Thresholding 阈值 125
3.9 Minimum Description Length Principle 最小描述长度原理 126
3.9.1 Bayesian Perspective 贝叶斯角度 127
3.9.2 Entropy and Information 熵和信息 128
4. Learning With Support Vector Machines SVM 利用支持向量机（SVM）学习130
4.1 Introduction 简介 130
4.2 Linear Discriminant Functions for Binary Classification
二元分类的线性判别函数 132
4.3 Perceptron Algorithm 感知机算法 136
4.4 Linear Maximal Margin Classifier for Linearly Separable Data
线性可分数据的最大边缘线性分类器 141
4.5 Linear Soft Margin Classifier for Overlapping Classes
重叠类的软边缘线性分类器 152
4.6 Kernel-Induced Feature Spaces 核函数引导的特征空间 158
4.7 Nonlinear Classifier 非线性分类器 162
4.8 Regression by Support Vector Machines 支持向量机回归 167
4.8.1 Linear Regression 线性回归 169
4.8.2 Nonlinear Regression 非线性回归 172
4.9 Decomposing Multiclass Classification Problem Into Binary Classification Tasks
将多类分类问题分解为二元分类任务 174
4.9.1 One-Against-All OAA 一对所有（OAA） 175
4.9.2 One-Against-One OAO 一对一（OAO）
內容試閱：
PREFACE
Over the past two decades, the field of Machine Learning has become one of the mainstays of information technology. Many successful machine learning applications have been developed, such as： machine vision image processing in the manufacturing industry for automation in assembly line, biometric recognition, handwriting recognition, medical diagnosis, speech recognition, text retrieval, natural language processing, and so on. Machine learning is so pervasive today that you probably use it several times a day, without knowing it. Examples of such ubiquitous or invisible usage include search engines, customer-adaptive web services, email managers spam filters, computer network security, and so on. We are rethinking on everything we have been doing, with the aim of doing it differently using tools of machine learning for better success.
Many organizations are routinely capturing huge volumes of historical data describing their operations, products, and customers. At the same time, scientists and engineers are capturing increasingly complex datasets. For example, banks are collecting huge volumes of customer data to analyze how people spend their money; hospitals are recording what treatments patients are on, for which periods and how they respond to them; engine monitoring systems in cars are recording information about the engine in order to detect when it might fail; worlds observatories are storing incredibly high-resolution images of night sky; medical science is storing the outcomes of medical tests from measurements as diverse as Magnetic Resonance Imaging MRI scans and simple blood tests; bioinformatics is storing massive amounts of data with the ability to measure gene expression in DNA microarrays, and so on. The field of machine learning addresses the question of how best to use this historical data to discover general patterns and improve the process of making decisions.
Terminology in the field of learning is exceptionally diverse, and very often similar concepts are variously named. In this book, the term machine learning has been mostly used to describe various concepts, though the terms： artificial intelligence, machine intelligence, pattern recognition, statistical learning, data mining, soft computing, data analytics when applied in business contexts, also appear at various places.
There have been important advances in the theory and algorithms that form the foundations of machine learning field. The goal of this text book is to present basic concepts of the theory, and a wide range of techniques algorithms that can be applied to a variety of problems. There are many machine learning algorithms not included in this book, that can be quite effective in specific situations. However, almost all of them are some adaptation of the algorithms included in this book. Self-learning will easily help to acquire the required knowledge.
Basically, there are two approaches for understanding machine learning field. In one approach, we treat machine learning techniques as a black box, and focus on understanding the problems tasks of interest： matching these tasks to machine learning tools and assessing the quality of the output. This gives us hands-on experience with machine learning from practical case studies. Subsequently, we delve into the components of this black box by examining machine learning algorithms a theoretical principle-driven exposition is necessary to be effective in machine learning. The second approach starts with the theory; this is then followed by hands-on experience.
The approach into the field of machine learning taken in this book has been the second one. We have focussed on machine learning theory. For hands-on experience, we propose to provide a platform through self-study machine learning projects.
In this book on Applied Machine Learning, the reader will get not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to challenging problems： learning how to conceptualize a prob
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