<p>Founder of the long-running Machine Learning Week conference, Eric Siegel, Ph.D. helps companies understand how machine learning and innovations in AI impact our businesses and our daily lives. </p><p>Eric Siegel, Ph.D. is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” and executive editor of<span style="color: black; font-size: 12pt;"> </span><em>The Machine Learning Times.</em><span style="color: black; font-size: 12pt;"> </span>He wrote the bestselling<span style="color: black; font-size: 12pt;"> </span><em>Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die,</em><span style="color: black; font-size: 12pt;"> </span>which has been used in courses at hundreds of universities, as well as<span style="color: black; font-size: 12pt;"> </span><em>The AI Playbook: Mastering the Rare Art of Machine Learning Deployment.</em><span style="color: black; font-size: 12pt;"> </span>Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice. </p><p>Eric has appeared on Bloomberg TV and Radio, BNN (Canada), Israel National Radio, National Geographic Breakthrough, NPR Marketplace, Radio National (Australia), and TheStreet. Eric and his first book have been featured in <em>Big Think, Businessweek, CBS MoneyWatch, Contagious Magazine, The European Business Review, The Financial Times, Forbes, Fortune, GQ, Harvard Business Review, The Huffington Post, The New York Review of Books, The New York Times, Newsweek, Quartz, Salon, The San Francisco Chronicle, Scientific American, The Seattle Post-Intelligencer, The Wall Street Journal, The Washington Post, </em>and <em>WSJ MarketWatch</em>. </p>
Machine Learning and AI Expert; bestselling author of <i>Predictive Analytics</i> and <i>The AI Playbook</i> (February 2024)
<ul><li><strong>How Machine Learning Delivers on the Promise of AI</strong></li><li>The excitement over machine learning and AI has reached a fever pitch. But what is the value, the function, the purpose? The most actionable win to be gained from data is prediction. This is achieved by analytically learning from data how to render predictions for each individual. Such predictions drive more effectively the millions of operational decisions that organizations make every day. In this keynote, Machine Learning Week founder and bestselling author Eric Siegel reveals how machine learning – aka predictive analytics – works and the ways in which it delivers value to organizations across industry sectors.</li><li><strong>The AI Playbook: How to Capitalize on Machine Learning</strong></li><li>The greatest tool is the hardest to use. Machine learning is the world’s most important general-purpose technology – but it’s notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption. In this keynote, bestselling author Eric Siegel presents the gold-standard, six-step practice for ushering machine learning projects from conception to deployment. And he illustrates the practice with stories of success and of failure, including revealing case studies from UPS, FICO, and prominent dot-coms. </li><li><strong>Most Machine Learning Projects Fail to Deploy – Here's the Remedy</strong></li><li>Industry leader Eric Siegel's latest research shows most models generated with machine learning to improve business operations in a new way never deploy. It turns out that machine learning operationalization – which changes existing processes in order to improve them – takes a lot more planning, socialization, and change-management efforts than most ever begin to realize. The problem is more in leadership than in technology. In this talk, Eric will outline the required practice needed to run ML projects so that they successfully deploy and deliver a business impact.</li><li><strong>How Machine Learning Reduces Risk in Financial Services</strong></li><li>The gold standard method for leveraging data to reduce risk – in credit, insurance, and other lines of business – is machine learning. The predictive models this technology generates reduce risk, cut costs, and boost profit. In this keynote address, bestselling author and former Columbia University professor Eric Siegel will clearly demonstrate exactly what is learned from data and how enterprises apply what's learned to improve the business metrics that matter most in the financial services sector.</li></ul>