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 https://probml.github.io/pml-b

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 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.