Book Chat: The Master Algorithm

The Master Algorithm is a description of the pros and cons of different machine learning techniques and the author’s quest to unify them into a single algorithm that can tackle any kind of problem. It has sections on five major kinds of learning algorithms: nearest neighbor, naive bayes, decision trees, support vector machines (SVM), and neural networks. It then covers Alchemy, the author’s attempt to unify multiple disparate styles of learning algorithms into a single overarching implementation. Overall the book succeeds at a popular science level of description of the current status of machine learning techniques, but it didn’t satisfy my needs as someone closer to the software.

The author declared an intention to keep the amount of math to a minimum. It ended up that the author tried to describe mathematical concepts in prose, and that didn’t work as well for me as just using some formulas probably would have. I wanted either enough math that I felt that I understood what was going on fully or no math at all so I didn’t feel like I had a mediocre partial understanding.

The descriptions of the individual learning algorithms made sense with a surface read. Once I stopped to try and really understand the differences it was less clear, I think the lack of hard formulas impeded my understanding. There was a section that described a unification of several different algorithms, but turned out to be a metaphor for early attempts to unify learning algorithms where the math never worked. The inclusion of this effort and the way it was described ended up being confusing to me.

The discussion of the real, but incomplete, unification scheme in Alchemy was interesting. The implication that you would still need an advanced degree in machine learning to use it says to me there is more to do. If machine learning is truly going to change the world the means of training models needs to be opened up to at least the average software engineer, if not business users.

I feel like the author’s particular perspective put him too close to the problem to really write a more popular science style book. I think he could have written the book I was looking for with more math and a more practical software aspect. From a technical prerequisite perspective this could be an excellent text for people who are trying to come at machine learning from a non-technical background.

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