I think to say the book has a heavy focus on frequentist statistics is a little misleading. Jaynes discusses a lot of frequentist methods but the emphasis is on doing so from a very Bayesian point of view.
He uses Bayesian methods in some cases, and non-Bayesian method in others. For instance on page 1412 he describes a problem for which Bayesian methods are "not appropriate."
http://omega.albany.edu:8008/ETJ-PS/cc14g.ps
This is a bit of an anathema to purist Bayesians like Radford Neal who say that Jaynes Maximum Entropy method is not consistent with Bayesian methods and that it "doesn't make any sense"
At Cambridge, you find high-level theoretical machine learning research in the Engineering, Computer Science and Physics faculties. (My spies inside the DAMTP don't point to much going on there - but who knows). None of the above ever seem to talk to each other, sadly.
