Imagine getting into your car and saying, “Take me to work,” and then enjoying an automated drive as you read the morning news. We are getting very close to that kind of machine learning scenario, and companies like Ford expect to have production vehicles in the latter part of 2020.
Driverless cars are just one popular example of this technology. It’s also used in countless applications such as predicting fraud, identifying terrorists, recommending the right products to customers at the right time, and correctly identifying medical symptoms to prescribe appropriate treatments.
The concept has been around for decades. What’s new is that it can now be applied to huge quantities of data. Cheaper data storage, distributed processing, more powerful computers and new analytical opportunities have dramatically increased interest in machine learning systems.
This paper is based on presentations given over the last few years. Wayne Thompson, Manager of Data Science Technologies at SAS, introduces key machine learning concepts, explains its correlation with statistics, and describes SAS solutions that enable machine learning at scale.
Download the report, courtesy of SAS, to further explore the role of statistics and how new technologies are applying machine learning to big data.
All information that you supply is protected by our privacy policy. By submitting your information you agree to our Terms of Use.
* All fields required.
