机器学习(贝叶斯和优化方法英文版原书第2版)(精)/经典原版书库
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作者(希)西格尔斯·西奥多里蒂斯|责编:曲熠
出版社机械工业
ISBN9787111668374
出版时间2021-01
装帧其他
开本其他
定价299元
货号31032013
上书时间2024-05-30
商品详情
- 品相描述：全新
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目录
Preface
Acknowledgments
About the Author
Notation
CHAPTER 1 Introduction
1.1 The Historical Context
1.2 Artificia Intelligenceand Machine Learning
1.3 Algorithms Can Learn WhatIs Hidden in the Data
1.4 Typical Applications of Machine Learning
Speech Recognition
Computer Vision
Multimodal Data
Natural Language Processing
Robotics
Autonomous Cars
Challenges for the Future
1.5 Machine Learning: Major Directions
1.5.1 Supervised Learning
1.6 Unsupervised and Semisupervised Learning
1.7 Structure and a Road Map of the Book
References
CHAPTER 2 Probability and Stochastic Processes
2.1 Introduction
2.2 Probability and Random Variables
2.2.1 Probability
2.2.2 Discrete Random Variables
2.2.3 Continuous Random Variables
2.2.4 Meanand Variance
2.2.5 Transformation of Random Variables
2.3 Examples of Distributions
2.3.1 Discrete Variables
2.3.2 Continuous Variables
2.4 Stochastic Processes
2.4.1 First-and Second-Order Statistics
2.4.2 Stationarity and Ergodicity
2.4.3 Power Spectral Density
2.4.4 Autoregressive Models
2.5 Information Theory
2.5.1 Discrete Random Variables
2.5.2 Continuous Random Variables
2.6 Stochastic Convergence
Convergence Everywhere
Convergence Almost Everywhere
Convergence in the Mean-Square Sense
Convergence in Probability
Convergence in Distribution
Problems
References
CHAPTER 3 Learning in Parametric Modeling: Basic Concepts and Directions
3.1 Introduction
3.2 Parameter Estimation: the Deterministic Point of View
3.3 Linear Regression
3.4 Classifcation
Generative Versus Discriminative Learning
3.5 Biased Versus Unbiased Estimation
3.5.1 Biased or Unbiased Estimation
3.6 The Cramer-Rao Lower Bound
3.7 Suffcient Statistic
3.8 Regularization
Inverse Problems: Ill-Conditioning and Overfittin
3.9 The Bias-Variance Dilemma
3.9.1 Mean-Square Error Estimation
3.9.2 Bias-Variance Tradeoff
3.10 Maximum Likelihood Method
3.10.1 Linear Regression: the Nonwhite Gaussian Noise Case
3.11 Bayesian Inference
3.11.1 The Maximum a Posteriori Probability Estimation Method
3.12 Curse of Dimensionality
3.13 Validation
Cross-Validation
3.14 Expected Loss and Empirical Risk Functions
Learnability
3.15 Nonparametric Modeling and Estimation
Problems
MATLAB? Exercises
References
CHAPTER 4 Mean-Square Error Linear Estimation
4.1 Introduction
4.2 Mean-Square Error Linear Estimation: the Normal Equations
4.2.1 The Cost Function Surface
4.3 A Geometric Viewpoint: Orthogonality Condition
……
CHAPTER 5 Online Learning: the Stochastic Gradient Descent Family of Algorithms
CHAPTER 6 The Least-Squares Family
CHAPTER 7 Classification: a Tour of the Classics
CHAPTER 8 Parameter Learning: a Convex Analytic Path
CHAPTER 9 Sparsity-Aware Learning: Concepts and Theoretical Foundations
CHAPTER 10 Sparsity-Aware Learning: Algorithms and Applications
CHAPTER 11 Learning in Reproducing Kernel Hilbert Spaces
CHAPTER 12 Bayesian Learning: Inference and the EM Algorithm
CHAPTER 13 Bayesian Learning: Approximate Inference and Nonparametric Models
CHAPTER 14 Monte Carlo Methods
CHAPTER 15 Probabilistic Graphical Models: Part Ⅰ
CHAPTER 16 Probabilistic Graphical Models: Part Ⅱ
CHAPTER 17 Particle Filtering
CHAPTER 18 Neural Networks and Deep Learning
CHAPTER 19 Dimensionality Reduction and Latent Variable Modeling
Index
内容摘要
本书对所有重要的机器学习方法和新近研究趋势进行了深入探索，通过讲解监督学习的两大支柱——回归和分类，站在全景视角将这些繁杂的方法一一打通，形成了明晰的机器学习知识体系。
新版对内容做了全面更新，使各章内容相对独立。
全书聚焦于数学理论背后的物理推理，关注贴近应用层
的方法和算法，并辅以大量实例和习题，适合该领域的科研人员和工程师阅读，也
适合学习模式识别、统计/自适应信号处理、统计/贝叶斯学习、稀疏建模和深度学习等课程的学生参考。
此外，本书的所有代码
均可免费下载，包含MATLAB和Python两个版本。
第2版重要更新·重写了关于神经网络和深度学习的章节，以反映自第1版以来的新进展。这一
章从感知器和前馈神经网络的基础概念开始讨论，对深度网络进行了深入研究，涵盖较新的优化算法、批标准化、正则化技术（如
Dropout方法）、CNN和RNN、注意力机制、对抗样本和对抗训练、胶囊网络、
生成架构（如RBM）、变分自编码器和GAN。
·扩展了关于贝叶斯学习的内容，包括非参数贝叶斯方法，重点讨论中国餐馆过程（CRP）和印度自助餐过程（IBP）。
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