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‘Crowdsourcing big data’ might sound like a randomly generated selection of buzz words, but it turns out to represent a powerful leap forward in the accuracy of predictive analytics. As companies and researchers are fast discovering, data prediction competitions provide a unique opportunity for advancing the state of the art in fields as diverse as astronomy, health care, insurance pricing, sports ratings systems and tourism forecasting. This session will focus not simply on the mechanics of data prediction competitions, but on why they work so effectively. As it turns out, the ‘why’ boils down to a couple of simple propositions, one associated with Archimedes and the other with world record-breaking sprinter Roger Bannister. Those propositions are not unique to the world of data science, but, as this session will show, have a particularly compelling application to it.
Now – President and Chief Scientist: Kaggle. Earlier – Founder: FastMail; Founder: Optimal Decisions. Long ago – Consultant: McKinsey & Co, A.T Kearney
Jeremy Howard is the President and Chief Scientist at Kaggle. Previously, he founded FastMail (sold to Opera Software) and Optimal Decisions (sold to ChoicePoint – now called LexisNexis Risk Solutions). Prior to that he worked in management consulting, at McKinsey & Company and A.T. Kearney, but he is now nearly fully recovered.
Jeremy’s passion is applying algorithms to data. At FastMail he used algorithms to automate nearly every part of the business – as a result the company only needed a total of 3 full time staff, and got over a million signups. Optimal Decisions was a business entirely built to commercialise a new algorithm he designed for the optimal pricing of insurance.
Jeremy competes regularly in data mining competitions, which he uses to test himself and stay on the leading edge of machine learning and predictive modelling technology. His competition performance history is available on his Kaggle profile page . If you have beaten Jeremy in a competition, he would appreciate it if you didn’t rub it in too much.
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