Subscribe to DSC Newsletter

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.

Source for the picture: click here

  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.

DSC Resources

Views: 4188

Replies to This Discussion

I don't believe I'm up to speed on machine learning and thought I'd check out the list, but at the top of the list is a statistical text I used in college and I'm familiar with one or two others which are also statistical texts.  Are you sure this is the right list?  I would have imagined that the machine learning list would have been focused on neural networks and such.

It's Mike Jordan's list and a bit outdated. Mike is a famous professor at Berkeley University. There's a come back of AI now, known as deep learning by ML practitioners.

For those not familiar with these abbreviations, ML is machine learning and AI is artificial intelligence.

RSS

On Data Science Central

© 2019   AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service