北大张志华推荐经典机器学习书

勿在浮沙筑高台
请仔细研读下列书籍

初阶课程

概率与统计

• [1] Larry Wasserman. All of Statistics
  

  All of Statistics

• [2] Morris H. DeGroot， Mark J. Schervish. Probability and Statistics
  

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• [3] T. W. Anderson John Wiley An Introduction to Multivariate Statistical Analysis
  

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• [4] R. J. Muirhead . Aspects of Multivariate Statistical Theory
  

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线性代数

• [1] Gilbert Strang. Introduction to Linear Algebra
  

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• [2] Trefethen N. Lloyd，David Bau lll.Numerical Linear Algebra
  

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机器学习课程

• [1] John D. Kelleher，Brian Mac Namee. Fundamentals of Machine Learning for Predictive Data Analytics
  

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• [2] Andrew R. Webb，Keith D. Copsey. Statistical Pattern Recognition
  

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• [3] Trevor HastieRobert TibshiraniJerome Friedman Elements of statistical learning
  

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中阶课程

数值优化

• [1] Jorge Nocedal and Stephen J. Wright. Numerical Optimization, second edition. Springer, 2006.
  

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• [2] Timothy Sauer. Numerical Analysis
  

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算法课程

• Michael Mitzenmacher，Eli Upfal. Probability and Computing: Randomized Algorithms and Probabilistic
  Analysis
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程序设计方面

• David B. Kirk，Wenmei W. Hwu. Programming
  Massively Parallel Processors: A Hands-on Approach, Second Edition
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高阶课程

1. Trefethen N. Lloyd and David Bau III. Numerical linear algebra. SIAM, 1997.
   

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2. Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to
   Algorithms. Cambridge Press, 2014.
   

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3. Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press.
   

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4. Jorge Nocedal and Stephen J. Wright. Numerical Optimization, second edition. Springer, 2006.

   
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5. Michael Mitzenmacher and Eli Upfal. Probability and Computing: Randomized Algorithms and
   Probabilistic Analysis. Cambridge University Press, 2005.
   

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6. Roger A. Horn and Charles R. Johnson. Matrix Analysis. Cambridge University Press, 1986.
   

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7. George Casella and Roger L. Berger. Statistical Inference, second edition. The Wadsworth Group,2002.
   

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8. Jonathan M. Borwein and Adrian S. Lewis. Convex Analysis and Nonlinear Optimization: Theory
   and Examples, second edition. Springer, 2006.
   

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进阶课程

• [1] Shai Shalew-Shwartz and Shai Ben-David. Understanding Machine Learning: from Theory
  to Algorithms. Cambridge University Press. 2014
  

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• [2] George Casella and Roger L. Berger. Statistical Inference, second edition. The Wadsworth
  Group, 2002.

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• [3] Andrew Gelman et al. Bayesian Data Analysis, Third edition. CRC, 2014.
  

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• [4] Daphne Koller and Nir Friedman. Probabilistic Graphical Models: Principles and
  Techniques. MIT, 2009.
  

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• [5] Jonathan M. Borwein and Adrian S. Lewis. **Convex Analysis and Nonlinear Optimization:

  
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Theory and Examples**, second edition. Springer, 2006.

• [6] Avrim Blum, John Hopcroft, and Ravindran Kannan. Foundation of Data Science. 2016.

• [7] Richaerd S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT, 2012.
  

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• [8] Thomas M. Cover and Joy A. Thomas. Elements of Information Theory. John Wiley &
  Sons, 2012.
  

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