Demystifying Machine Learning and Artificial Intelligence

Oct

1

1:00 - 2:30 p.m ET

Machine learning. Artificial intelligence. Big data. Industry 4.0.

These buzzwords are everywhere – in newspapers, magazines, TV and podcasts. But what do they mean?

Definitions vary, depending on who you ask. And some of the claims are sensational: Artificial intelligence (AI) and machine learning (ML) are “dark magic” that will soon automate our jobs away. Really?

No. In many cases, these buzzwords are just new names for tried-and-true programs, approaches and techniques, but adapted to take advantage of higher data availability. For example, the supervised and unsupervised learning at the heart of ML rely on well-established statistical modeling techniques: regression, classification and clustering.

Join us for a keynote talk and panel discussion about these topics. You’ll find guidance and stories about how to get started with ML techniques or effectively integrate them within existing programs that seek to make the most of your data and drive more value for your organization.

Featured Keynote

David Hand

David J. Hand

Senior Research Investigator and Emeritus Professor of Mathematics, Imperial College London

David Hand, PhD, is a Fellow of the British Academy and a recipient of the Guy Medal of the Royal Statistical Society. He has served two terms as President of the Royal Statistical Society and is also the author of The Improbability Principle and Dark Data. Hand was made OBE for services to research and innovation in 2013.

Panel Discussion

Building useful models with data has always been mission-critical for companies striving to compete through innovation. Due to Industry 4.0, big data and digitalization initiatives, you now have more complex data than ever before. Hear examples from cutting-edge organizations that are solving problems more quickly and driving innovation more effectively during a discussion with:

  • Sam Gardner, Wildstat Consulting
  • Cameron Willden, W. L. Gore
  • Jason Wiggins, SAS

What will you learn?

Data is messy. And if the goal of modeling is to increase understanding rather than to simply predict, you also need to know what the data represents. Before you can build a useful model, you need to know how to clean dirty data, make it analysis-ready, and communicate and share your findings to drive action. So a mastery of ML techniques is just one component of solving problems more quickly and driving innovation more effectively.

You'll gain a better understanding of:

 
  • The life cycle of an empirical model, from data to deployment.
  • The different types of empirical models, their structure, strengths and weaknesses.
  • Common ML techniques and models, and how they relate to empirical models.
  • Best practices for building predictive and explanatory models quickly.
  • How to start ML initiatives or integrate them within existing programs.
  • What should be automated – and what shouldn’t.

Meet the Speakers

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