Machine Learning Book

I did not feel like this book provided any good information, and it felt padded to boot. In fact, after a while I lost patience and started skimming.

For instance, several pages were wasted on an explanation of an algorithm that could have been given in a single paragraph. Then time was spent on a very awkward proof involving the relationship between sums of SPECIFIC numbers, which one could simply test directly. The author may have made some point about the awkwardness of the proof, but there was no point to this. This was doubly damning because it undermined the author's argument that testing couldn't cover general correctness; so why waste time on a bad proof of a specific case that could be tested directly?

The rest of the book was this awkward. Discussions about comments in steps of proofs were given, yet no significant ties to program development were produced. When the author finally got to Hoare triples, I didn't understand why the previous pages were even written as none of the material could even be seen as a support to this.

The book is very short, even with all the padding; much of the book's length is dedicated to answers to the exercises. This leads me to doubt that there was enough material for a book. Overly long examples, pointless proofs and material that doesn't build up are the mark of a lack of substance. This material probably could have been covered in a single article.