Bayesian statistics and applied computing at CMU?

A reader asks:

I wanted to pick your brain about stats and machine learning at CMU … I’m considering a Ph.D. in a finance or a related discipline.

Here’s the thing, I’m very much attracted to schools with established inter-disciplinary programs, like CMU’s additional masters in machine learning, and Duke’s supplemental masters in statistics. Duke bills itself as the best Bayesian shop under the sun, which is also attractive. I’m not dogmatically fixed on Bayesian methods, but I do find it a much more natural way of thinking, and more naturally applied to practical problems.

One person I spoke to suggested that Duke was the better program based on my interests, but I read on your blog that Bayesian methods and applied computing are pretty well represented at CMU, so I figured I’d get your thoughts. Leaving aside the reality that one can’t choose where they’re admitted, and that one should focus their choice on the strength of their primary department, I’d like to know which would be the better option.

My response:

I admit I don’t know much about the finance program here, nor about the supplemental masters in ML. And of course I know even less about Duke’s equivalent programs.

That said, CMU is absolutely a strong place for machine learning and statistics, including applied computing and Bayesian statistics.

Bayes:

The core courses for the ML masters (10-701, 10-702, and 10-705) do cover Bayesian methods and inference. We study the basic theory and plenty of applications, including less-often-taught methods like Bayesian nonparametrics. Parts of 702 and 705 are especially helpful for clarifying how Frequentist and Bayesian inferences differ. Although I’m a fan of the Bayesian approach, I really appreciate how Larry Wasserman challenges us to understand its weaknesses thoroughly, using plenty of examples where Classical methods have an advantage over Bayesian ones (such as Sec 12.6 here).
Beka Steorts also offers a pair of courses that go into more depth on Bayesian theory and applications.
There’s also a close link between the Statistics and Philosophy departments: particularly Kadane and Schervish here in Stats, and Seidenfeld in Phil, work together regularly on the foundations of statistical inference, incl. Bayesian.

ML and applied computing:

CMU is also rare (maybe unique?) in that we have a whole college of Computer Science with a whole department of Machine Learning (…rather than just a department of CS with just a ML research group). That means we have a ton of faculty and students with both applied and theoretical interests. Also, our theory courses account for how to optimize an estimator or algorithm, and how long it will take to converge, not merely whether it’s statistically a good estimator.
The Statistics department also encourages applications, not just theory. The undergrads get courses like Statistical Computing and Undergraduate Advanced Data Analysis. There’s a whole MS in Statistical Practice. And for the PhD students like myself, our first major research project (the Advanced Data Analysis) is very much intended to be an applied project.
There’s a constant stream of interesting applied talks on campus, more of them than you can hope to attend 🙂 including Stat Bytes, Machine Learning Lunch, Machine Learning & Social Sciences group, SIAM student chapter, Statistical Machine Learning Reading Group, Statistics in Education Research Group, …

I’m sure that you’d find CMU worthwhile if you end up coming here.