Agree with you. But none of this is useful for practical (applied) machine learning. I don't want to disappoint you but you can read it as machine learning porn, but otherwise don't waste time on it.
I mean, as a graduate student, it was definitely incredibly useful. As a practicing data scientist, I’d have to say that it’s also incredibly useful.
I’ve used this stuff, and more often, the ideas taught, to break down a problem into a tackle-able set of pieces more times than I can count.
Never underestimate the fundamentals. Too many of my colleagues use models without actually understanding any of it. I’ve debugged so many problems by looking at the technical details in original papers and textbooks.
Yes, unless you are among 20 top researchers who are working on frontier of ml. Bayesian probabilistic techniques does not work or are very slow for any practical purpose.
Oh crumbs! There I was thinking that by obtaining an estimate of the probabilities of the responses of different groups to an employee survey I was applying a bayesian probalistic approach.
I'm going to have to rethink everything now as since it worked and was quite quick (I didn't even sample using MCMC, just brute force pulled permutations) so it was clearly not a bayesian approach, and I am very very far from one of the top 20 (or 200, or 2000 or 20000, maybe 200000?) researchers...
I’ve used this stuff, and more often, the ideas taught, to break down a problem into a tackle-able set of pieces more times than I can count.
Never underestimate the fundamentals. Too many of my colleagues use models without actually understanding any of it. I’ve debugged so many problems by looking at the technical details in original papers and textbooks.