(1) Modern classification algorithms like SVMs need some pretty hardcore math routines (SVMs require Quadratic Programming, which isn't trivial to implement correctly). Do you intend to implement these yourself? If so, that alone might be useful as a separate library, with the ML library built on top of it.
(2) I've been thinking about a JS distributed computing library for a long time -- sort of like Folding@Home, but instead of having to download a program, you just visit a website and let JS do the crunching (Ajax will pull and push data chunks). With modern JS engines, this has become more of a reality. So to bring it back to your question -- why not try to abstract as much of the math+algorithms from as many distributed computing projects as possible, and then build a generic JS library for doing distributed computation. You could have each distributed computing project as a benchmark -- i.e., start by implementing SETI@Home using your library, then move on to Folding@Home. I guarantee you won't be bored... ;)
Good luck, and I'm really glad people are pushing the capabilities of JS these days.
On a lighter note, can you imagine the derisive laughter if you had suggested this 10 years ago? :)
I also point you out to some interesting resources about concepts that you could easily implement in your library.
Like the NGD ( Normalized google distance ), just an idea, to make smarter tag clouds ? http://www.complearn.org/
If you are planning to release this as a product, i doubt that it could gain traction, although the whole node.js thing makes me wonder whether everything is moving to the client, even heavy computational tasks as ML or AI problems. If it is a project just for the sake of it or for fun, then it would be cool to see your implementation.