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369 hunch net-2009-08-27-New York Area Machine Learning Events


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Introduction: Several events are happening in the NY area. Barriers in Computational Learning Theory Workshop, Aug 28. That’s tomorrow near Princeton. I’m looking forward to speaking at this one on “Getting around Barriers in Learning Theory”, but several other talks are of interest, particularly to the CS theory inclined. Claudia Perlich is running the INFORMS Data Mining Contest with a deadline of Sept. 25. This is a contest using real health record data (they partnered with HealthCare Intelligence ) to predict transfers and mortality. In the current US health care reform debate, the case studies of high costs we hear strongly suggest machine learning & statistics can save many billions. The Singularity Summit October 3&4 . This is for the AIists out there. Several of the talks look interesting, although unfortunately I’ll miss it for ALT . Predictive Analytics World, Oct 20-21 . This is stretching the definition of “New York Area” a bit, but the train to DC is reasonable.


Summary: the most important sentenses genereted by tfidf model

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1 I’m looking forward to speaking at this one on “Getting around Barriers in Learning Theory”, but several other talks are of interest, particularly to the CS theory inclined. [sent-4, score-0.623]

2 Claudia Perlich is running the INFORMS Data Mining Contest with a deadline of Sept. [sent-5, score-0.156]

3 This is a contest using real health record data (they partnered with HealthCare Intelligence ) to predict transfers and mortality. [sent-7, score-0.667]

4 In the current US health care reform debate, the case studies of high costs we hear strongly suggest machine learning & statistics can save many billions. [sent-8, score-1.295]

5 Several of the talks look interesting, although unfortunately I’ll miss it for ALT . [sent-11, score-0.351]

6 This is stretching the definition of “New York Area” a bit, but the train to DC is reasonable. [sent-13, score-0.103]

7 This is a conference of case studies of applications of ML to real-world problems. [sent-14, score-0.344]

8 30, and we already have several speakers lined up. [sent-19, score-0.371]


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