Machine Learning (Theory)


Key Scientific Challenges

Yahoo released the Key Scientific Challenges program. There is a Machine Learning list I worked on and a Statistics list which Deepak worked on.

I’m hoping this is taken quite seriously by graduate students. The primary value, is that it gave us a chance to sit down and publicly specify directions of research which would be valuable to make progress on. A good strategy for a beginning graduate student is to pick one of these directions, pursue it, and make substantial advances for a PhD. The directions are sufficiently general that I’m sure any serious advance has applications well beyond Yahoo.

A secondary point, (which I’m sure is primary for many :) ) is that there is money for graduate students here. It’s unrestricted, so you can use it for any reasonable travel, supplies, etc…

3 Comments to “Key Scientific Challenges”
  1. Sergiu says:

    Is the program intended for mid / late stage graduate students, or do beginners that just started their work in grad school have a chance too?

  2. jl says:

    There isn’t a crisp answer to this, but the closest one is probably “mid”. Too late, and there is little point as graduation is imminent. Too early, and it may be hard to compete due to lack of a research track record.

  3. […] machine learning efforts in oil well drilling.  The list was made by John Langford, which comment that the challenges are general enough to have applications outside Yahoo. And indeed, three of the  five challenges mirror challenges in analysing drilling time […]

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