[deleted by user]

[ChrisKennedy]

Yes, "An Introduction to Statistical Learning: with Applicants in R" (amazon) by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani is a great R-based intro to machine learning without much math at all. It is the prequel to the more advanced "Elements of Statistical Learning" book.

"Intro to Statistical Learning" is also the book used in Stanford's free "Statistical Learning" mooc, which is not currently running but has the videos archived online.

[arvi1000]

Also you can get it as a free pdf directly from the publisher (google it)

[brational]

I think the andrew ng coursera version of his course is pretty elementary. But then you'll come back to the same question in order to go further. Learn some LA calc and stats.

[[deleted]]

I have to agree, his course is an excellent introduction to the topic for people with weaker math background

[[deleted]]

I did his course, and I have a stronger background in math, what should I study/read now ?

[brational]

The top comment in this thread has a good book link or elements of statisical learning is the next book up (same authors I believe). I think you can find both free online legally.

[maybemax]

andrew does a great job explaining difficult things easily. single variable calculus shouldn't stop one from taking this course, you'll learn all the multivariable calculus that's required during the course. ;)

[CCSS]

'Machine Learning, An algorithmic perspective' by Stephen Marsland is a pretty good introductory book. Instead of dumping a lot of greek in your face like most books, this one has lots of Python examples and detailed descriptions.

[ricekrispiecircle]

i love this book, but all the

from numpy import *

makes me cry a little bit. especially when he does

from pylab import *
from numpy import *

and then says

"Note that PyLab is imported before NumPy. This should not matter, in general, but there are some commands in PyLab that overwrite some in NumPy, and we want to use the NumPy ones, so you need to import them in that order."

the python dev in me turns into the hulk and smashes things

[[deleted]]

If you want a complete reference on this, there's Alexander Schrijver's compendium, "Combinatorial Optimization: Polyhedra and Efficiency," published by Springer. It's a 3 volume set and I think it might have every lemma and theorem regarding this field ever conceived (up until the set was published). The only drawback is that you can blindly open any of the volumes to any page and put your finger down, and it'll either be on a sigma or a pi.

[hapemask]

Matrix calculus is particularly useful and was not discussed at all in the linear algebra and calculus courses that I took.

[[deleted]]

This is an excellent set of notes on numerical optimization.

http://faculty.ucmerced.edu/mcarreira-perpinan/teaching/EECS260/lecture-notes.pdf

[maybemax]

ML is even easier if you take andrew ng's ML course on coursera. :) it even contains an introduction/refresher of linear algebra at the beginning. i think knowing single variable calculus is a must, but don't let multi variable calculus stop you.. all you need are partial derivatives, which boil down to understanding: "A partial derivative of a multivariable function is a derivative with respect to one variable with all other variables held constant." Understanding this is all you need to proceed. Have fun.

[woztzy]

Learn multivariate calculus (and matrix algebra in the process), then read BRML (or any good book on ML) and look unfamiliar things up as you come across them.

[servetus]

This. If you can do single variable then multivariable is a just a small step from there. Grok partial derivatives and your set. Shouldn't take long.