chrishwiggins · August 29, 2015 14:09 · djhsu · Nov 22, 2014
diff --git a/applied ML books? b/applied ML books?
 Q: what book should i use to learn ML?

 A: use several, and find the one that speaks to you.

 the list below assumes you know a bit of math but
 are not very mathematical, and are interested in learning
 enough to be practical. that is, it is not at the
 mathematical level of MIJ's alleged list
 (cf. https://news.ycombinator.com/item?id=1055389 )

 my advice is to read the same thing discussed in several
 books, and you will find that the 3rd or 4th (or sometimes
 5th) different way of saying the same thing speaks to you.

 you should start with a book close to your training.

 i hope to add more books to this list but briefly

 1. if you were raised in statistics, start with ESL
 http://www.amazon.com/gp/product/0387848576
 (available for free: http://statweb.stanford.edu/~tibs/ElemStatLearn/ )

 2. if you were raised in physics, Start with Bishop
 http://www.amazon.com/gp/product/0387310738

 2.5 unless you really love statistical physics or information theory, then MacKay might be an easier point of entry
 http://www.amazon.com/gp/product/0521642981


 3. if you were raised in CS, start with Kevin Murphy
 http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020

 4. if you're more mathematical than the above, but not a mathematician,
 start with Mohri
 http://www.amazon.com/gp/product/026201825X

 5. if you're a mathematician, go to MIJ's list.
	Q: what book should i use to learn ML?

	A: use several, and find the one that speaks to you.

	the list below assumes you know a bit of math but
	are not very mathematical, and are interested in learning
	enough to be practical. that is, it is not at the
	mathematical level of MIJ's alleged list
	(cf. https://news.ycombinator.com/item?id=1055389 )

	my advice is to read the same thing discussed in several
	books, and you will find that the 3rd or 4th (or sometimes
	5th) different way of saying the same thing speaks to you.

	you should start with a book close to your training.

	i hope to add more books to this list but briefly

	1. if you were raised in statistics, start with ESL
	http://www.amazon.com/gp/product/0387848576
	(available for free: http://statweb.stanford.edu/~tibs/ElemStatLearn/ )

	2. if you were raised in physics, Start with Bishop
	http://www.amazon.com/gp/product/0387310738

	2.5 unless you really love statistical physics or information theory, then MacKay might be an easier point of entry
	http://www.amazon.com/gp/product/0521642981


	3. if you were raised in CS, start with Kevin Murphy
	http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020

	4. if you're more mathematical than the above, but not a mathematician,
	start with Mohri
	http://www.amazon.com/gp/product/026201825X

	5. if you're a mathematician, go to MIJ's list.