Machine Learning and Data Science for Economists

 

November 6-7, 2019 

2 days, 8:30 AM – 4:30 PM
Hyatt Regency
Seattle, WA

This course aims to speak to the value of using methods from machine learning and data science for the applied business economist.  More course information

Registration Details

NABE Member*: $1,500

U.S. Government Employee*: $1,575

Non-Member*: $1,650

*Early-Bird Deadline: October 2, 2019

To be eligible for a refund less $50 fee, registration cancellation must be received in writing by October 2, 2019.  Questions? Please contact NABE at nabe@nabe.com or phone 202-463-6223.


REGISTER ONLINE

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and scan/email to nabe@nabe.com or fax 202-463-6239. Registration by email/fax includes a $25 processing fee.  Save $25 by registering online! 

Course Location:

Hyatt Regency
808 Howell Street
Seattle, WA 98101


About the Instructor



Matthew Harding
is an Econometrician and Data Scientist who develops machine learning and artificial intelligence techniques to answer Big Data questions related to individual consumption and investment decisions in areas such as health, energy, and finance. He is an Associate Professor of Economics and Statistics at UC Irvine. He holds a PhD in Economics from MIT and an MPhil in Economics from Oxford University. He directs the Deep Data Lab which conducts research into cutting edge econometric methods for the analysis of “deep data”, large and information-rich data sets derived from many seemingly unrelated sources to provide novel economic insights. At the same time his research emphasizes solutions for achieving triple-win strategies.  These are solutions that not only benefit individual consumers, but are profitable for firms, and have a large positive impact on society at large. Professor Harding advised a number of companies and agencies on economics and data science problems, including Apple, US Commodity Futures Commission, World Bank, Electric Power Research Institute (EPRI), and the Department of Justice. He is also an advisor to a number of technology startups.