Mike Jordan 推荐的13本机器学习书籍 | 数盟

Berkeley的Mike Jordan推荐阅读的13本机器学习相关图书,偏学术。

Mike Jordan at Berkeley recommends the following books. The list is definitely on the more rigorous side (aimed at more researchers than practitioners), but going through these books (along with the requisite programming experience) is a useful, if not painful, exercise. This list of intermediate-level books was published a few years ago, but is still interesting.

  1. Casella, G. and Berger, R.L. (2001). “Statistical Inference” Duxbury Press.
  2. Ferguson, T. (1996). “A Course in Large Sample Theory” Chapman & Hall/CRC.
  3. Lehmann, E. (2004). “Elements of Large-Sample Theory” Springer.
  4. Gelman, A. et al. (2003). “Bayesian Data Analysis” Chapman & Hall/CRC.
  5. Robert, C. and Casella, G. (2005). “Monte Carlo Statistical Methods” Springer.
  6. Grimmett, G. and Stirzaker, D. (2001). “Probability and Random Processes” Oxford.
  7. Pollard, D. (2001). “A User’s Guide to Measure Theoretic Probability” Cambridge.
  8. Durrett, R. (2005). “Probability: Theory and Examples” Duxbury.
  9. Bertsimas, D. and Tsitsiklis, J. (1997). “Introduction to Linear Optimization” Athena.
  10. Boyd, S. and Vandenberghe, L. (2004). “Convex Optimization” Cambridge.
  11. Golub, G., and Van Loan, C. (1996). “Matrix Computations” Johns Hopkins.
  12. Cover, T. and Thomas, J. “Elements of Information Theory” Wiley.
  13. Kreyszig, E. (1989). “Introductory Functional Analysis with Applications” Wiley.



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