I agree with plinkplonk, but if you'd like examples: 1) I mentioned multidimensional scaling elsewhere. In the book there is implementation and it doesn't eve

I agree with plinkplonk, but if you'd like examples: 1) I mentioned multidimensional scaling elsewhere. In the book there is implementation and it doesn't even say what the goal is (in terms of objective function) and/or how well it performs. There are many different goals of multidimensional scaling. If one understand even the objective function they are minimizing then the algorithm to do so will make more sense. 2) The book grazes over SVM's and makes a few blanket statements that I haven't heard from serious researchers. E.g., "[t]he one that is usually recommended ... is called the radial-basis function. The radial-basis function is like the dot-product in that it takes two vectors and returns a value. Unlike the dot-product, it is not linear and can thus map more complex spaces." Most of the kernel/svm folks I know would never be so confident in saying such a blanket statement as it entirely relies on the data/situation.