Information technology professionals become the 'trainers' behind the algorithms.

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Few people are as well-positioned as technologists to understand how artificial intelligence is changing the world. After all, IT professionals are accustomed to exploring new automation technologies with the potential for transforming how work gets done.

“In the machine learning paradigm, you have to learn about how computers can be trained to do certain tasks,” says Naren Ramakrishnan,the Thomas L. Phillips Professor of Engineering at Virginia Tech and director of its Discovery Analytics Center. “If you’re going to work in Amazon’s Whole Foods Market division, you might be thinking of training a computer to recognize vegetables. If you’re going to be working in a bank, you have to train an algorithm to be good at understanding financial transactions and loans,” he says. “In a sense, you become the human element — the trainer, if you will — behind the algorithm.”

As part of Virginia Tech’s Online Master of Information Technology program, Ramakrishnan teaches the popular course Machine Learning with Big Data.

Ramakrishnan uses machine learning examples from his collaborations with companies, agencies and other organizations. For example, a partnership between the Discovery Analytics Center and the Washington Post resulted in the creation of a system to help the publisher predict the popularity of its articles, sometimes even before they’re published. A week of class is used to explore that collaboration: what data was used, how the project unfolded, how the model was developed and evaluated, and how it was finally applied. “That’s of great interest,” he says. “A lot of people learn machine learning as an academic curiosity, but very few people know how it’s actually deployed in practice.”

Such practical insights are an important part of the instruction students get, Ramakrishnan says. As another example, he cites the issue of model drift. “One thing that happens when you deploy machine learning is it appears to work great, but then over time life changes, and the model tends to drift and not stay in tune with reality. You need to start updating your model —  but how and how frequently should you update your model?” he says. “We talk about these issues of keeping the model current — making sure your predictions and recommendations are still valid — and how you can do that at scale.”

Something that’s interesting to Ramakrishnan is the diverse backgrounds of his students.  No matter what industry they are in or intend to work in, everybody has a use for what they learn in this course, he says. That’s useful when it’s time to work on a team project. Students divide into groups and use virtual meetings to develop a problem involving an interesting data set, either public or proprietary. The goal is to develop a predictive application, algorithm or task that makes sense for that particular domain.

“There are a lot of entry points into machine learning,” Ramakrishnan says. “Machine learning is almost like a Swiss Army knife. You never know which tool you’ll need, but they’re all useful somewhere.”

Virginia Tech’s Online Master of Information Technology program is offered jointly by the College of Engineering and the Pamplin College of Business. Ranked by U.S. News & World Report as the No. 2 “Best Online Graduate Computer Information Technology Programs” the past four years.