- 机器学习的算法观点(第2版)(英文版)
- - 作者：(新西兰)史蒂芬·马斯兰|责编:陈亮
  - 出版社：世图出版公司
  - ISBN：9787519295707
  - 出版日期：2022/08/01
  - 页数：437
- 售价：51.6

内容大纲
《机器学习的算法观点》是一部介绍机器学习算法的书籍。本书在阐述与机器学习的数学和统计学理论的同时，提供了相关的编程实践和实验。第2版新增了深度信念网络和高斯过程的章节、卡尔曼滤波器和粒子滤波器的附加讨论，对支持向量机的内容进行修订，并且对代码进行改进。目录：前言、预先准备、神经元、神经网络和线性判别、多层感知器、径向基函数和样条、降维、概率学习、支持向量机、优化和搜索、进化学习、强化学习、特征树学习、集成学习、非监督学习、马尔科夫链蒙特卡洛方法、图模型、对称权值和深度置信网络、高斯过程。
作者介绍
目录
Prologue to 2nd Edition
Prologue to 1st Edition
CHAPTER 1  Introduction
  1.1  IF DATA HAD MASS, THE EARTH WOULD BE A BLACK HOLE
  1.2  LEARNING
    1.2.1  Machine Learning
  1.3  TYPES OF MACHINE LEARNING
  1.4  SUPERVISED LEARNING
    1.4.1  Regression
    1.4.2  Classification
  1.5  THE MACHINE LEARNING PROCESS
  1.6  A NOTE ON PROGRAMMING
  1.7  A ROADMAP TO THE BOOK
  FURTHER READING
CHAPTER 2  Preliminaries
  2.1  SOME TERMINOLOGY
    2.1.1  Weight Space
    2.1.2  The Curse of Dimensionality
  2.2  KNOWING WHAT YOU KNOW: TESTING MACHINE LEARNING AL-GORITHMS
    2.2.1  Overfitting
    2.2.2  Training, Testing, and Validation Sets
    2.2.3  The Confusion Matrix
    2.2.4  Accuracy Metrics
    2.2.5  The Receiver Operator Characteristic (ROC) Curve
    2.2.6  Unbalanced Datasets
    2.2.7  Measurement Precision
  2.3  TURNING DATA INTO PROBABILITIES
    2.3.1  Minimising Risk
    2.3.2  The Naive Bayes' Classifier
  2.4  SOME BASIC STATISTICS
    2.4.1  Averages
    2.4.2  Variance and Covariance
    2.4.3  The Gaussian
  2.5  THE BIAS-VARIANCE TRADEOFF
  FURTHER READING
  PRACTICE QUESTIONS
CHAPTER 3  Neurons, Neural Networks, and Linear Discriminants
  3.1  THE BRAIN AND THE NEURON
    3.1.1  Hebb's Rule
    3.1.2  McCulloch and Pitts Neurons
    3.1.3  Limitations of the McCulloch and Pitts Neuronal Model
  3.2  NEURAL NETWORKS
  3.3  THE PERCEPTRON
    3.3.1  The Learning Rate 7/
    3.3.2  The Bias Input
    3.3.3  The Perceptron Learning Algorithm
    3.3.4  An Example of Perceptron Learning: Logic Functions
    3.3.5  Implementation
  3.4  LINEAR SEPARABILITY
    3.4.1  The Perceptron Convergence Theorem

    3.4.2  The Exclusive Or (XOR) Function
    3.4.3  A Useful Insight
    3.4.4  Another Example: The Pima Indian Dataset
    3.4.5  Preprocessing: Data Preparation
  3.5  LINEAR REGRESSION
    3.5.1  Linear Regression Examples
  FURTHER READING
  PRACTICE QUESTIONS
CHAPTER 4  The Multi-layer Perceptron
  4.1  GOING FORWARDS
    4.1.1  Biases
  4.2  GOING BACKWARDS: BACK-PROPAGATION OF ERROR
    4.2.1  The Multi-layer Perceptron Algorithm
    4.2.2  Initialising the Weights
    4.2.3  Different Output Activation Functions
CHAPTER 5  Radial Basis Functions and Splines
CHAPTER 6  Dimensionality Reduction
CHAPTER 7  Probabilistic Learning
CHAPTER 8  Support Vector Machines
CHAPTER 9  Optimisation and Search
CHAPTER 10  Evolutionary Learning
CHAPTER 11  Reinforcement Learning
CHAPTER 12  Learning with Trees
CHAPTER 13  Decision by Committee: Ensemble Learning
CHAPTER 14  Unsupervised Learning
CHAPTER 15  Markov Chain Monte Carlo (MCMC) Methods
CHAPTER 16  Graphical Models
CHAPTER 17  Symmetric Weights and Deed Belief Networks
CHAPTER 18  Gaussian Processes
APPENDIX A  Python
Index

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