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I've only read (parts of) the Murphy book from 2012, I assume you're reading this one: https://probml.github.io/pml-book/toc1.pdf?

Almost all of the content that the new book covers, with the exception of the third part on deep learning, is about theory that was almost exclusively invented before 2012. Classical ML (non deep learning) is actually very rigorous compared to modern ML. There exist good theorems (statistical learning theory) for most of the classical models I'm aware of.



Yes that’s the one. Good to know, thanks for making me aware!




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