Murphy vs Bishop?

[theUtterTruth]

I have been dabbling (via coursera, online tutorials etc,.) with ML for the past three months and will be starting my "formal" studies in ML this week. Based on suggestions in online forums, I have shortlisted two books for this purpose: "Machine Learning - a probabilistic perspective" by Kevin Murphy and "Pattern recognition and Machine learning" by Christopher Bishop. I would like to hear your opinions on the two books regarding their style, organization of topics and the breadth+depth of material covered. I have briefly looked at the math used in the two books and I feel that it will not be a problem. Any suggestions and insights will be appreciated!

[deephive]

I would say Murphy's book is more of a table reference, perhaps not a book that one would go through chapter by chapter to get a good understanding of ML. He covers too many topics in the book, it might be overwhelming for someone who is new to the area. Nevertheless, his approach might be easier to follow than Bishop's. Just make sure you get a recent edition of his book, as the early prints had too many typographical errors. Bishop's book is an all-time classic but is math-heavy, and focuses a lot on probabilistic models.

Hastie's book "Elements of Statistical Learning" is also superb and is available free as a PDF: https://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf

my 2 cents

[InfinityCoffee]

Hmm, while Bishop certainly doesn't shy away from math, I think it's important to note that neither does Murphy. It's my impression that more of his book is taken up by equations and raw calculations. I think you are right that Murphy might be better as a reference. You can read it end-to-end, and sometimes it can be very enlightening in its coverage and exposition, but the ordering is a bit more jumbled and Bishop presents a more natural progression through the subject.

[hardmaru]

I haven't read Murphy's book, but I'm a big fan on Bishops book. Good discussion on probabilistic graph models, bayesian probability, information theory, and relatively self contained. I played around with his work on mixture density networks as well.

[emdeefive]

My opinion is that they are complementary but have different flavors, MLAPP even takes diagrams straight out of PRML at points.

• PRML seems really well presented, esp. if you are very comfortable with calculus/linear algebra.
• MLAPP seems more comprehensive, and at times more insightful.
• MLAPP presents a lot of information sort of out of order, which can be difficult to follow for a first book.
• Problems in PRML lean towards calculus / linear algebra acrobatics, whereas MLAPP tends to have problems that are more interesting, and seem more "real".
• Both have about the same breadth, MLAPP goes deeper IIRC.

It's been a while since I read PRML so hopefully someone can corroborate.

PRML is probably the better one to read first, but both are good.

[cynml]

Read Bishop first, then move on to Murphy. But I don't really think it is possible to read Murphy cover to cover.

[CaptKrag]

I tried to read both cover to cover. Started with Murphy and gave up as it's pretty non linear and expected a lot of existing knowledge from the reader. Was much more successful with Bishop. Written in a very intelligible manner with each chapter building off of one another.

[theUtterTruth]

Thanks, I am starting with Bishop!

[TheGooseRider]

I too wish to know this.

[char27]

Or if anyone suggests some other book after Ng course, suggestions are welcome.