linkstream2 microblog

http://www.newyorker.com/online/blogs/elements/2013/04/steamrolled-by-big-data.html

http://www.freep.com/usatoday/article/1975777

http://m.guardiannews.com/news/datablog/2013/mar/15/twitter-users-tribes-language-analysis-tweets

Some thoughts on a good paper giving intuition on machine learning approaches

http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
http://dl.acm.org/citation.cfm?id=2347755

In particular, the paper gives good intuition about:

– overfitting (e.g. how it’s related to multiple testing & bias v variance)
– the curse of dimensionality (in high-D all neighbors look the same)
– the non-practicality of theoretical guarantees
– how different frontiers can give the same prediction
– ensembles (which reduce variance greatly without increasing bias that much)
– ensembles vs Bayesian model averaging (which essentially select the best model)

http://www.illumina.com/platinumgenomes/
A family trio (NA12877, NA12878, and NA12882) sequenced on a HiSeq 2000 system. An individual (NA18507) sequenced on a HiSeq 2500 system.

homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
http://dl.acm.org/citation.cfm?id=2347755