These are cut’n’pasted from my Coursera Computaional Investing class forum. Not yet examined, just here to follow up on.

I don’t know what Prof. Tucker chose for his company but highstock (http://www.highcharts.com/stock/demo/) is in my opinion the best around (free for non commercial products). In any case here is a list of other good visualization libraries:

http://g.raphaeljs.com/
http://www.jqplot.com/
http://plugins.jquery.com/project/gchart
http://vis.stanford.edu/protovis/
http://polymaps.org/
http://code.google.com/p/flot/

Are you prepared?

Take a look at Everything You Wanted to Know About Machine Learning, But Were Too Afraid To Ask, that’s a good place to start. It is an article on the big ml blog about an article (meta article?). Charles Parker summarizes Pedro Domingos’ short paper A Few Useful Things to Know about Machine Learning, which is what to read next.

I’m not real sure about this next one: A First Encounter with Machine Learning which is a 93-page pdf from Max Welling. The first paragraph of the preface explains the focus of the book. This may be an early version though; there are “??” references to figures and the bibliography is empty. Be warned, very “mathy”.

Prepared? Prepared for what? Yeah, this is where we’re going. Andrew Ng’s 10-week Machine Learning class starts on Coursera April 22. Lots of good reviews for this online.

Not yet scheduled but also very interesting looking is Geoffrey Hinton’s Neural Networks for Machine Learning, also on Coursera. Hinton was recently swallowed up by Google, but maybe not entirely. Let’s hope Coursera runs this again this year.

Tons more links to follow are in the  Stack Overflow post Overwhelmed by Machine Learning—is there an ML101 book?