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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?

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