A fair amount of machine learning research in recent years has focused around "automated machine learning", in which the computer itself attempts to accomplish many of the engineering and optimization steps usually left to the human programmer. Automating away tasks that are time-consuming or complex is always a worthwhile idea, but how much more useful do these automations really make machine learning? In this talk, I'll argue that automated machine learning is only a weak proxy for what we really want, and that recent methods don't get us much closer to the true goal.
Charles Parker is the Vice President of Machine Learning Algorithms at BigML. He holds a Ph.D. in computer science from Oregon State University. He was previously a research associate at the Eastman Kodak Company where he applied machine learning to image, audio, video, and document analysis. He also worked as a research analyst for Allston Holdings, a proprietary stock trading company, developing statistically-based trading strategies for U.S. and European futures markets. His current work for BigML is in the areas of Deep Learning and Bayesian Parameter Optimization.