Machine Learning on the Cheap and Easy(thunderboltlabs.com)
thunderboltlabs.com
Machine Learning on the Cheap and Easy
http://thunderboltlabs.com/posts/machine-learning-on-the-cheap-and-easy
5 comments
For getting started I really liked Toby Segarin's Programming Collective Intelligence. It was my introduction to this area before I went on to produce After the Deadline.
I read that one, I liked the fact that he builds up each example from first principles. It's hard to find explanations that bridge theory and practice.
this is like recipes for doing machine learning, not deep enough.
Thanks for the link!
Not to try to highjack the post, but for those interested in resources for computer vision see my post from a few months ago: http://colinlea.posterous.com/on-self-guided-study-of-comput...
Not to try to highjack the post, but for those interested in resources for computer vision see my post from a few months ago: http://colinlea.posterous.com/on-self-guided-study-of-comput...
Good stuff - I added http://szeliski.org/Book/ to my reading list, thanks for sharing the link. There's a huge overlap for certain classes of problems. CV in many ways resembles the same problems with large online data streams, noisy, time critical, huge volumes of data - feature extraction is problematic. Hybrid solutions usually required.
I've found that reading books from other ML domains helps out in understanding the application and getting ideas on how to approach the problem.
I've found that reading books from other ML domains helps out in understanding the application and getting ideas on how to approach the problem.
As I experienced it, Szeliski's book is better as a reference as it covers lots of material (just see the number of citations at the end). I don't think it's an easy read without reading (some of) the cited papers (or having background knowledge).
I think the two free online courses provided by Stanford last year is really good for beginners.
I found the Stanford course almost assumed too much of a stats background to make it easily accessible. Starting with the math foundations is sound, but scary for people who don't dream in LaTeX :)
also, an HN'er posted his notes on Andrew Ng's class on ML...I learnt more from this than from the videos, as the videos take much too long.
link: http://holehouse.org/mlclass/
link: http://holehouse.org/mlclass/
It's also one of the courses complete with materials in the new iTunes U app.
Thanks for the head's up... been meaning to watch it as a refresher.
I highly recommend the 'elements of statistical learning' but also Bishop's 'pattern recognition and machine learning'
I just added it to my list of books to review. Thanks for mentioning it. What did you like about Bishop's Book?
These are really, really basic tools and books. Once you're past this you can get a copy of some good Springer books (e.g. "Recommender Systems Handbook") and follow up on the papers and studies referenced.
I woundn't consider The Elements of Statistical Learning Theory a (very) basic book. It covers plenty of material in relatively good depth.