Perhaps you could explain in your own words why exactly it is clear that the ML book you are reading is "manifestly inferior" to your preferred approach?
There is a bit of confusion here. I'm not stating that frequentist machine learning is inferior to Bayesian machine learning. I'm stating that Bayesian probability is superior to frequentist probability.
How do I say this? Because in all the case that I know, either a Bayesian model can be reduced to a frequentist one or a Bayesian model gives more accurate prediction.
That said, not even this is a problem. Since I'm learning the subject, I'm not at the stage of saying "this sentence is wrong". I'm at the stage of "this sentence doesn't make sense in the context of Bayesianism". So I'm asking "is there a book that teaches ML from a Bayesian point of view?".
The answer I'm discovering, appallingly but maybe not so, is no.
As for the fervent defence, under the premises elucidated in the comments, I hold none of the myths, so it doesn't apply.
Because in all the case that I know, either a Bayesian model can be reduced to a frequentist one or a Bayesian model gives more accurate prediction.
I typically see this stated as "there is a Bayesian interpretation for every effective statistical technique." As pointed out elsewhere, typically people use "frequentist" to mean "non-Bayesian," which is not particularly effective as a classification.
...So I'm asking "is there a book that teaches ML from a Bayesian point of view?".
The answer I'm discovering, appalling
If it's worth saying, but not worth its own post (even in Discussion), then it goes here.
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