mantapoint
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Comment · 3 years ago
Comment · 3 years ago
If you're more inclined to theory, I would suggest "Learning Theory from First Principles" by F. Bach: https://www.di.ens.fr/~fbach/ltfp_book.pdf
The book assumes limited knowledge (similar to what is required for Pattern Recognition I would say) and gives a good intuition on foundational principles of machine learning (bias/variance tradeoff) before delving to more recent research problems. Part I is great if you simply want to know what are the core tenets of learning theory!
Anyways, I still believe that learning foundational stuff such as the bias-variance tradeoff is useful before diving to more advanced stuff. I even think that tackling recent research question with old tool is insightful too. But that's only my opinion, and perhaps I'm in denial :)