The Benefits of Evolutionary Search for Building Forecasting Models
The Benefits of Evolutionary Search for Building Forecasting Models

Abstract: 

Some data science teams are implementing evolutionary search to discover underlying time series relationships, infer causes and effects, and improve the accuracy of forecasts. Michael Schmidt, founder and CTO of Nutonian, will share some of the breakthroughs in A.I. that have made evolutionary search possible at scale. He'll also discuss best practices for forecasting and share how a Fortune 500 retailer is predicting sales and optimizing its supply chain.

Bio: 

Michael Schmidt's research focuses on "Machine Science" - a direction in artificial intelligence research to accelerate data-driven discovery. Over the past 6 years, he has worked on algorithms and techniques to automate knowledge discovery from data. In particular, he has published extensively on identifying mathematical relationships (such as laws of physics) in experimental data, and algorithms in evolutionary computation. Michael is the creator of the Eureqa project - a popular software program for discovering hidden mathematical relations in experimental data. His research has appeared in several news outlets from the New York Times, to NPR's RadioLab, and Communications of the ACM. Currently, Michael runs Nutonian Inc. which specializes in scientific data mining and cloud computing for data analysis. In 2011, Michael was featured in the Forbes list of the "World’s 7 Most Powerful Data Scientists" by Tim O'Reilly.

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