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Machine learning allows us to detect subtle correlations in large data sets, and use those correlations to make accurate predictions. However, these subtle correlations are often spurious - they exist only in a particular dataset - and the resultant model performs poorly, or gives unexpected results in the real world. Moreover, reasoning based on spurious correlations is dangerous.  Business decisions should be based on things that are true, not things that are true only in a limited dataset. The trouble, of course, is identifying what is spurious and what is not. In this webinar, we’ll explain how combining causal inference with machine learning can help us address these problems.

We’ll cover:
  • when you should think about causality and lessons to apply in  your data science practice

  • the latest research at the intersection of machine learning and causality

  • how causal thinking helps us write models that generalize to new circumstances, including an example of the causal approach applied to a computer vision problem

Along the way, we’ll discuss the ethical implications of causality, and answer all your questions on our live webinar.  We look forward to seeing you there!

Speakers


Research Engineer

Nisha Muktewar

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Nisha Muktewar is a Research Engineer at Cloudera Fast Forward Labs, where she spends time researching latest ideas in machine learning and builds prototypes that showcase these capabilities when applied to real-world use cases. In her previous life, she has led teams in designing, building, and implementing predictive modeling solutions for pricing, consumer behavior, marketing mix, and customer segmentation use cases for insurance and retail/consumer businesses. She holds a Bachelor of Engineering degree in computer science from University of Pune, India.

Research Lead

Chris Wallace

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Chris Wallace is the Research Lead at Cloudera Fast Forward, where he works on making breakthroughs in machine intelligence accessible and applicable in the “real world.” He has experience doing data science in organizations both large (the UK NHS) and small (as the first employee at a tech startup). Chris likes building data products and cares deeply about making technology work for people, not vice versa. He holds a PhD in particle physics from the University of Durham.

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