Top critical review
13 people found this helpful
Avoid if want you systematically learn the subject of ML
on 16 January 2017
I'm currently a physics PhD student and have a fairly strong mathematics background. I decided I was going to sit down with a textbook and make extensive notes in order to gain an intuition of machine learning, before moving on to doing some more 'hands on' practise with real data sets. I chose Murphy due to the probabilistic framework - it sounded like a nice and unifying way of the concepts of machine learning and the other reviews were very positive.
Three chapters in, I was really enjoying it. It seemed well written and I felt like I was gaining a lot of intuition. After that though, it went downhill... The author will introduce symbols without explaining what they are, as well as equations. In addition to being difficult to understand, there's a significant amount of typos.
As someone who wanted to systematically work through the entire book and gain a deep understanding of the topic, this textbook simply does not cut it. If you already have a large amount of experience with machine learning, then I believe this book would make a good reference. If you're planning on using this book to learn, then I would avoid.