Eric Siegel is a leading consultant and former Columbia University professor. He is the founder of the popular Predictive Analytics World and Deep Learning World conference series.  

In this episode, Eric shares his decades of experience in predictive analytics. He discusses why ML is useful, and how predictive analytics have been used in business. Eric shares his view on prescriptive analytics, AI, and also explains uplift-modelling concepts, and why it is hard and so powerful. 

Eric's Recommendations

Books:

• Competing on Analytics: Updated with a New Introduction, The New Science of Winning by Thomas H. Davenport, Jeanne G. Harris, 2017
• Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst, by Dean Abbot 
• Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel 

Papers: 

• Sculley, David, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. "Hidden technical debt in machine learning systems." Advances in neural information processing systems 28 (2015). 
• Elder IV, John F. "The generalization paradox of ensembles." Journal of Computational and Graphical Statistics 12, no. 4 (2003): 853-864.