Machine Learning (Theory)


The Large Scale Learning class notes

The large scale machine learning class I taught with Yann LeCun has finished. As I expected, it took quite a bit of time :-). We had about 25 people attending in person on average and 400 regularly watching the recorded lectures which is substantially more sustained interest than I expected for an advanced ML class. We also had some fun with class projects—I’m hopeful that several will eventually turn into papers.

I expect there are a number of professors interested in lecturing on this and related topics. Everyone will have their personal taste in subjects of course, but hopefully there will be some convergence to common course materials as well. To help with this, I am making the sources to my presentations available. Feel free to use/improve/embelish/ridicule/etc… in the pursuit of the perfect course.


COLT and ICML registration

Sebastien Bubeck points out COLT registration with a May 13 early registration deadline. The local organizers have done an admirable job of containing costs with a $300 registration fee.

ICML registration is also available, at about an x3 higher cost. My understanding is that this is partly due to the costs of a larger conference being harder to contain, partly due to ICML lasting twice as long with tutorials and workshops, and partly because the conference organizers were a bit over-conservative in various ways.



Adam Kalai points out the New England Machine Learning Day May 1 at MSR New England. There is a poster session with abstracts due April 19. I understand last year’s NEML went well and it’s great to meet your neighbors at regional workshops like this.


I’m a bandit

Sebastien Bubeck has a new ML blog focused on optimization and partial feedback which may interest people.


NYU Large Scale Machine Learning Class

Yann LeCun and I are coteaching a class on Large Scale Machine Learning starting late January at NYU. This class will cover many tricks to get machine learning working well on datasets with many features, examples, and classes, along with several elements of deep learning and support systems enabling the previous.

This is not a beginning class—you really need to have taken a basic machine learning class previously to follow along. Students will be able to run and experiment with large scale learning algorithms since Yahoo! has donated servers which are being configured into a small scale Hadoop cluster. We are planning to cover the frontier of research in scalable learning algorithms, so good class projects could easily lead to papers.

For me, this is a chance to teach on many topics of past research. In general, it seems like researchers should engage in at least occasional teaching of research, both as a proof of teachability and to see their own research through that lens. More generally, I expect there is quite a bit of interest: figuring out how to use data to make predictions well is a topic of growing interest to many fields. In 2007, this was true, and demand is much stronger now. Yann and I also come from quite different viewpoints, so I’m looking forward to learning from him as well.

We plan to videotape lectures and put them (as well as slides) online, but this is not a MOOC in the sense of online grading and class certificates. I’d prefer that it was, but there are two obstacles: NYU is still figuring out what to do as a University here, and this is not a class that has ever been taught before. Turning previous tutorials and class fragments into coherent subject matter for the 50 students we can support at NYU will be pretty challenging as is. My preference, however, is to enable external participation where it’s easily possible.

Suggestions or thoughts on the class are welcome :-)

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