Four short links: 3 June 2014

Machine Learning Mistakes, Recommendation Bandits, Droplet Robots, and Plain English

  1. Machine Learning Done Wrong[M]ost practitioners pick the modeling algorithm they are most familiar with rather than pick the one which best suits the data. In this post, I would like to share some common mistakes (the don’t-s).
  2. Bandits for RecommendationsA common problem for internet-based companies is: which piece of content should we display? Google has this problem (which ad to show), Facebook has this problem (which friend’s post to show), and RichRelevance has this problem (which product recommendation to show). Many of the promising solutions come from the study of the multi-armed bandit problem.
  3. Dropletsthe Droplet is almost spherical, can self-right after being poured out of a bucket, and has the hardware capabilities to organize into complex shapes with its neighbors due to accurate range and bearing. Droplets are available open-source and use cheap vibration motors and a 3D printed shell. (via Robohub)
  4. Apple’s App Store Approval Guidelines — some of the plainest English I’ve seen, especially the Introduction. I can only aspire to that clarity. If your App looks like it was cobbled together in a few days, or you’re trying to get your first practice App into the store to impress your friends, please brace yourself for rejection. We have lots of serious developers who don’t want their quality Apps to be surrounded by amateur hour.
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