Expert guidance on making the most of machine learning
The Seven Deadly Sins of Machine Learning
Dick De Veaux of Williams College shares some of his experiences with machine learning as a consultant in science and industry and as a teacher in the classroom. Through industry case studies, he shows some of the problems with how people use machine learning and the hype surrounding it.
De Veaux begins with the mistakes we can make when we fail to properly define the problem. “People dive into a big data set without a sense of why they’re doing it, or what the problem is,” says De Veaux. He also discusses the enormous task of preparing the data and warns us not to take too much pride in our methods.
Watch to learn more about the seven deadly sins of machine learning:
- Failing to define the problem.
- Underestimating data preparation.
- Ignoring what’s not there.
- Falling in love with your model.
- Ignoring the data pedigree.
- Confusing correlation and causation.
- Taking pride in your methods.
Demystify the buzzwords and expand your toolkit
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.
So how can some of the buzzwords like machine learning and artificial intelligence help you solve problems and innovate? Industry experts demystify these terms and discuss some of the things you should do if you want to use these techniques well, including collaboration with other disciplines, knowing your data pedigree and taking time to expand your toolkit.
You’ll hear from:
- Dick De Veaux, Williams College
- Julia O’Neill, Direxa Consulting
- Russ Wolfinger, SAS