Competitive Predictive Modeling: and how to win $ 3,000,000

Jan 24, 2013 · Utrecht, Netherlands

The time of average, standard models is long gone. The world is getting more data-driven every day. This brings tremendous opportunities for the best predictive models to cut waste, delight customers, outdo the competition, and to make the world a better place. Based on this premise, Kaggle has seen rapid growth as a platform for competitive predictive modeling. Kaggle attracts the top minds to build algorithms for Allstate, Nasa, Wikipedia, Microsoft Kinect, Facebook, Merck, and others for large sums of money.

17:30 - 18:30 Drinks, Pizza
18:30 - 19:15 Kaggle war stories I - Tim Salimans
Tim Salimans is a Kaggle top contender who has won Kaggle competitions, generally places high, and is in the running to win $ 3,000,000. Tim will introduce us to the world of competitive predictive modeling: a world of intrigue, ensemble learning, collaborative modeling, and late night hacking. Tim often wields the sword of Bayesian learning, but will also pick other techniques if needed.

19:30 - 20:15 Kaggle war stories II - Laurens van der Maaten
Our second speaker, Laurens van der Maaten, is a postdoc at the Delft University of Technology, and recently won Kaggle's Merck Molecular Activity visualization Challenge.
Laurens will also share his Kaggle war stories and talk about t-Distributed Stochastic Neighbor Embedding(t-SNE). t-SNE is a new dimensionality reduction routine that is well suited for the visualization of high-dimensional datasets. This method, that he pioneered, was used in the winning submission. Laurens will also shed light on how t-SNE compares to other dimensional reduction algorithms like PCA and SVD.

20:15 - 21:00 Drinks

Call for speakers: talk to us about your interesting Data Science projects!

Event organizers
  • Data Science NL

    Data Science NL is a community dedicated to sharing knowledge about the exciting new multidisciplinary field of data science. Our community consists of people from businesses, startups, and academia that apply statistics and machine-learning to data problems that range from a recommender system for a startup, a solution that reduces costs for an enterprise, to a predictive algorithm that saves...

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