- 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
- - 作者：(希)西格尔斯·西奥多里蒂斯|责编:曲熠
  - 出版社：机械工业
  - ISBN：9787111668374
  - 出版日期：2021/01/01
  - 页数：1130
- 售价：119.6

内容大纲
    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）。
作者介绍
西格尔斯·西奥多里蒂斯（Sergios Theodoridis），雅典大学教授，研究兴趣包括机器学习、模式识别和信号处理等。他是IEEE（电气和电子工程师学会）和EURASIP（欧洲信号处理协会）的会士，并担任IEEE信号处理会刊的主编。曾获2014年IEEE信号处理杂志最佳论文奖，2009年IEEE计算智能协会杰出论文奖，以及2014年EURASIP最有价值服务奖等。此外，他还是经典畅销著作《模式识别》的第一作者。
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

内容大纲

作者介绍

目录

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