I really enjoy this book precisely because it focuses on many basics that are glossed over in more contemporary books. For example, while every knows what a ra

I really enjoy this book precisely because it focuses on many basics that are glossed over in more contemporary books. For example, while every knows what a random forest is, I wonder how many of those people actually know how to design and implement a decision tree, the building blocks of random forests. It also spends a lot more time with the theory behind machine learning, which many more contemporary books gloss over. Computational learning theory, and specifically topics like PAC learning rarely get the attention they should. I would also like to add that no academic book in ML truly becomes "irrelevant" or "out of date". In 2007 someone asking same question might have criticized the book for spending too much time on Neural Networks since they aren't really relevant any more (if you're unaware, NNs were largely viewed as a limited novelty in light of more recent developments in SVMs and RandomForests in the early 2000s).