Why Enrollment Is Surging in Machine Learning Classes
Machine learning is sweeping through industries, transforming scientific research and already changing aspects of everyday life, making the impossible possible. No wonder university students are flocking to it.
Enrollment in machine learning classes is soaring, and universities are scrambling to add classes to meet an unprecedented demand. Comprehensive figures aren’t available for U.S. machine learning enrollment or degrees, but all evidence points to dramatic growth. In a survey of more than a half dozen universities, all reported sharp increases in students pursuing the field.
“At its highest level, machine learning is about understanding the world through data,” said Geoffrey Gordon, acting chair of Carnegie Mellon University’s Machine Learning Department. “Anything you can think of — public policy, finance, automobiles and robotics, for example — there’s a role for machine learning.”
600% Rise at Carnegie Mellon
At Gordon’s school, enrollment in the graduate course “Introduction to Machine Learning” has soared nearly 600 percent in the past five years. Applicants to its machine learning Ph.D. program have doubled in six years. Last spring, the university added its first undergraduate course on the topic.
Other universities are seeing a similar spike in demand. At the University of California, Berkeley, enrollment in “Introduction to Machine Learning,” an undergraduate course, nearly tripled in less than two years.
The number of applicants to the university’s artificial intelligence degree programs (which include machine learning) is climbing, too. It nearly doubled in the past five years, according to figures supplied by Berkeley’s Department of Electrical Engineering and Computer Science.
AI Now a Third of Berkeley’s Electrical Engineering, Comp Sci Applicants
More tellingly, applicants for AI degree programs now comprise 34 percent of Berkeley’s EECS applicants, about double that of five years ago, said Stuart Russell, a professor of computer science and former department chairman.
Russell and others attribute machine learning’s popularity in part from the rise of deep learning, driven by the convergence of new algorithms, access to vast troves of data and powerful GPUs.
One of the fastest-growing types machine learning, deep learning is behind autonomous vehicles, better-than-human image recognition, real-time language translation and other once impossible feats. It holds huge potential for industries such as healthcare, energy, financial services, manufacturing and entertainment.
A hot job market is also fueling student demand. Web-services giants like Google, Facebook, Microsoft, IBM and Baidu are vying for talent, as are automakers, Wall Street and a host of startups.
“A Very Salable Skill”
“Many students see deep learning as the next big thing,” said Bill Dally, NVIDIA chief scientist and former chairman of Stanford University’s the computer science department. “They’re eager to learn this new technology, which is at the center of many things. Many also see it as a very salable skill.”
The University of Michigan offered a single undergraduate machine learning class in 2013. Today, there are two classes with nearly five times as many students, according to Department of Electrical Engineering and Computer Science statistics.
For the University of Toronto, the turning point came in 2011. That’s when student enrollment first outstripped expected demand for its graduate-level introductory machine learning class. That year, the difference was just a few percentage points, computer science department figures show. This fall, enrollment was 40 percent more than estimates.
“In the old days, you had to take an introductory computer class so you’d know how to use a computer at work,” said Lynne E. Parker, division director for the Information and Intelligent Systems Division at the National Science Foundation. “Today, students are recognizing that whatever their chosen field, there’s going to be some automation of the knowledge work — and that’s machine learning.”