Optimization, Machine Learning and Intelligent Experimentation with Michael McCourt

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

About This Episode

Today we’re joined by Michael McCourt the head of engineering at SigOpt. In our conversation with Michael, we explore the vast space around the topic of optimization, including the technical differences between ML and optimization and where they’re applied, what the path to increasing complexity looks like for a practitioner and the relationship between optimization and active learning. We also discuss the research frontier for optimization and how folks think about the interesting challenges and open questions for this field, how optimization approaches appeared at the latest NeurIPS conference, and Mike’s excitement for the emergence of interdisciplinary work between the machine learning community and other fields like the natural sciences.

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Thanks to our Sponsor!

Today’s show is brought to you by our good friends at SigOpt. Building effective models is a scientific process that requires experimentation to get right. With SigOpt, modelers design novel experiments, explore modeling problems and optimize models to meet multiple objective metrics in their iterative workflow. Whether tracking your training runs or running at scale hyperparameter optimization jobs, SigOpt is designed to meet your needs. Learn why teams from PayPal, Two Sigma, OpenAI, Numenta, Accenture and many more rely on SigOpt by signing up to use SigOpt for free forever at sigopt.com/signup.

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