[YeGoblynQueenne]

I read TESL during my Master's and I remember being very confused with the way it described decision tree learning. I remember being pleased with myself that I had a strong grip on decision tree learning before reading TESL and then being thoroughly confused after reading about them on TESL.

Eyballing the relevant chapter again (9.2) I think that may have been because it introduces decision tree learning with CART (the algorithm), whereas I was more familiar with ID3 and C4.5. Perhaps it's simpler to describe CART as TESL does, but decision trees are a propositional logic "model" (in truth, a theory) and for me the natural way to describe them, is as a propositional logic "model" (theory). I also get the feeling that Quinlan's work is sidelined a little, perhaps because he was coming from a more classical AI background and that's poo-poo'd in statistical learning circles. If so, that's a bit of a shame and a bit of an omission. Machine learning is not just statistics and it's not just AI, it's a little bit of both and one needs to have at least some background in both subjects to understand what's really going on. But perhaps it's the data mining/ data science angle that I find a bit one-sided.

Sorry to digress. I'm so excited when people discuss actual textbooks on HN.