Two December Graduates First to Receive Machine Learning Specialization

The new concentration prepares CS undergraduates to work on the frontiers of technology

In December 2020, Mateo Elezi and Maya Fuchs became the first University of Maryland students to earn a bachelor’s degree in computer science with a specialization in machine learning. A new offering from the Department of Computer Science, the specialization prepares students to work and study at the frontiers of technology where machine learning leads the way. 

In machine learning, computers use algorithms and statistical models to identify patterns in data that enable them to make decisions and perform tasks such as guiding self-driving cars, predicting our media preferences, diagnosing illnesses and protecting banks from fraudulent activity. In 2020, LinkedIn reported that hiring growth for specialists in machine learning has grown 74% annually in the past 4 years.

For Fuchs, who took as many machine learning courses as she could throughout her time at UMD, the official machine learning specialization will help distinguish her from her peers early in her career. 

“I have been interested in and working with machine learning for a while,” Fuchs said, “and I had done everything I could to make machine learning my career. The specialization came out in my last semester, and I'd already taken almost all of the requirements, so I jumped on the opportunity.”

Like Fuchs, Elezi is glad to have official recognition for concentrating his courses in the machine learning field. 

“Having the specialization highlights the difference between the work I have done and a standard computer science track, and it’s nice to have this recognized,” Elezi said. “The specialization provides a strong foundation of skills and gives me a great deal of experience both in understanding and applying machine learning concepts and methodologies.”

The machine learning specialization requires students to take introductory courses in data science, artificial intelligence and machine learning. In addition, students take upper-level, machine learning-specific electives and two courses selected from specialty topics like computer vision, computational game theory, deep learning, natural language processing, robotics and perception.

“For students who want to get into jobs working in machine learning or move into a Ph.D. in machine learning, this specialty sets them up with more sophisticated methods than they would learn in a typical undergraduate class,” said Tom Goldstein, an associate professor in computer science and the University of Maryland Institute for Advanced Computer Studies

Goldstein, who helped develop the specialization, created and teaches the upper-level elective CMSC 498P: Machine Learning Capstone. It’s a semester-long research project where students develop an open-source tool with a real-life application. During the course, students work with partners in industry such as Capital One and JPMorgan Chase on problems like detecting fraudulent transactions and analyzing events in the stock market.

“This class and the other upper-level machine learning electives that students take with this specialization will be very helpful for graduating students who want to work on those really complicated frontier systems at the bleeding edge of technology,” Goldstein said. 

That’s exactly where Elezi and Fuchs see themselves working. Elezi is currently interviewing for jobs as a software engineer and machine learning scientist and is considering going to graduate school after a few years in the workforce.

“I feel like the applications of this technology are limitless,” Elezi said. “One area of high interest to me is the intersection of machine learning and health, particularly within disease recognition, where machine learning has shown high potential for success.”

As for Fuchs, she is now working as a data scientist at the technology consulting firm Booz Allen Hamilton.

“I'm working specifically with natural language processing at the moment,” she said, “but my goal is to explore all facets of machine learning.”

With the rapid growth of the machine learning field, both Fuchs and Elezi should have plenty of opportunities coming their way, and with the new machine learning specialization officially associated with their degree, they already have a head start down their chosen paths.




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