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I haven't seen this book before but I did take Prof. Mohri's class a few years ago. His material is great but be warned that is's biased heavily towards theory
by iskander 14y ago
I haven't seen this book before but I did take Prof. Mohri's class a few years ago. His material is great but be warned that is's biased heavily towards theory (proofs and bounds instead of the more common/practical "rules of thumb"). You can get a lot done using machine learning tools having only a superficial familiarity with VC dimension, complexity bounds, etc... If however, you want to get deeper insight into the algorithms you're using or develop new algorithms yourself then Prof Mohri's of rigor is very useful.
- iskander 14y agoI wish the window for fixing a comment was a bit longer...
- achompas 14y agoI took Prof. Mohri's class this spring, and we actually used a draft version of this book. I agree with you that it's very theoretical, but Bishop, Mitchell, and the other ML resources don't address learning theory. VC-dimension isn't for everyone, but if you need to know about it, this is the book you want to use. There's also the added bonus of this text coming from Prof. Mohri et al.'s experiences at Google, so there's a lot of discussion about online algorithms and ranking (which you don't see in many other places).