北大張志華推薦經典機器學習書

勿在浮沙築高臺
請仔細研讀下列書籍

初階課程

概率與統計

• [1] Larry Wasserman. All of Statistics
  

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

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

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

線性代數

• [1] Gilbert Strang. Introduction to Linear Algebra
  

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

機器學習課程

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

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

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

中階課程

數值優化

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

• [2] Timothy Sauer. Numerical Analysis
  

算法課程

• Michael Mitzenmacher，Eli Upfal. Probability and Computing: Randomized Algorithms and Probabilistic
  Analysis
  

程序設計方面

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

高階課程

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

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

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

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

   
   

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

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

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

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

進階課程

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

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

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

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

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

  
  

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.
  

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