Not this Michael Jordan, that Michael Jordan.

There’s a machine learning reading list by Michael Jordan that’s been floating around on Hacker News for a few years, and in a recent AMA he added a few more. Full list:

• Casella, G. and Berger, R.L. (2001). “Statistical Inference” Duxbury Press.
• Ferguson, T. (1996). “A Course in Large Sample Theory” Chapman & Hall/CRC.
• Lehmann, E. (2004). “Elements of Large-Sample Theory” Springer.
• Gelman, A. et al. (2003). “Bayesian Data Analysis” Chapman & Hall/CRC.
• Robert, C. and Casella, G. (2005). “Monte Carlo Statistical Methods” Springer.
• Grimmett, G. and Stirzaker, D. (2001). “Probability and Random Processes” Oxford.
• Pollard, D. (2001). “A User’s Guide to Measure Theoretic Probability” Cambridge.
• Durrett, R. (2005). “Probability: Theory and Examples” Duxbury.
• Bertsimas, D. and Tsitsiklis, J. (1997). “Introduction to Linear Optimization” Athena.
• Boyd, S. and Vandenberghe, L. (2004). “Convex Optimization” Cambridge.
• Golub, G., and Van Loan, C. (1996). “Matrix Computations” Johns Hopkins.
• Cover, T. and Thomas, J. “Elements of Information Theory” Wiley.
• Kreyszig, E. (1989). “Introductory Functional Analysis with Applications” Wiley.
• A. Tsybakov. “Introduction to Nonparametric Estimation“
• Y. Nesterov. “Introductory Lectures on Convex Optimization“
• A. van der Vaart. “Asymptotic Statistics“
• B. Efron. “Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction“

Lots of theoretical stuff, to which we might want to add the more applied classics, i.e. Bishop, Mackay, Murphy, and Tibshirani. How many can you check off?