Wednesday, July 30, 2014

Why I wouldn't use Murphy's book to teach a Machine Learning Class

I was going through Murphy's Machine Learning book to remember some ML concepts that I needed. While I had seen it really fast, and the sections I am most familiarized with are well written. I found out that the book feels very rushed. The online erratas are huge, and each reprinting just seems to have more.

These mistakes might be passable if you are familiarized with the material, but if you are learning it, and taking the equations stated there at face value you might have some issues.

Also, terminology is not clearly explained, and for the sake of saving space, he refers you back to the very first time he introduced it, which most of the time is one of the very first chapters. I kid you not, I had to go back three times to find out he never really explained one symbol in the equation.

I used mostly Bishop's book to start doing Machine Learning, and in contrast to Murphy's, it is a pretty self contained book, he goes extensively through doing a restatement of most of the nomenclature he uses through the book, which is useful if you don't feel like going back to the very beginning to figure out what is he talking about.

Some sections, like the sampling, is very well explained, and way better than in Bishop's, or any other book. However, the sampling examples assume that the reader is familiarized with his particular terminology, which just takes more time than it should.

But for an entry level Grad student, or even an undergraduate, the book is just not friendly enough, the code is not really well documented, so aside from reproducing the figures in the book, there is really not much aggregated value in having it available, most of the time you'll spend most of your time just figuring out what the code does, and since it is not cross referenced with the equations in the book, is not really tractable as a learning experience.

It also has its issues as a reference book, since as I told you, it does too much back tracking when it comes to equations.

Some good things, are its explanation of Boltzmann Machines, which Bishop just really lacks, mostly because it was written after the deep net boom.

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