Duke Engineering Announces New Minor in Machine Learning & Artificial Intelligence

October 1, 2020

To help meet the growing demand for professionals trained machine learning and AI theory and practice, Duke Engineering is now offering a Minor in Machine Learning & Artificial Intelligence to undergraduates across the university

students working on computer

To help meet the growing demand for professionals trained machine learning and AI theory and practice, Duke Engineering is now offering a Minor in Machine Learning & Artificial Intelligence to undergraduates across the university. 

When we think about machine learning or artificial intelligence, the first thing that probably comes to mind is self-directed robots and self-driving cars. But many other fields rely on the smart manipulation of huge amounts of data—including medical pathology, communications, materials design and numerous others. 

“Employers want to develop better solutions to the problems that they already care about and they want to do it more efficiently,” said Duke professor of electrical and computer engineering Henry Pfister, who chaired the ECE faculty committee that led the development process. “Machine learning can sometimes help with both of these goals.”

Proving this demand in the job market, the career site Indeed listed “machine learning engineer” at the top of its “Best Jobs in the US: 2019” list, noting a 344% growth in the number of jobs posted on the site from 2014-2018, and an average salary of around $146,000.

From the steady student demand for machine learning classes, the appearance of several new student machine learning clubs across the university and the demonstrated success of the existing concentration available to ECE majors and the 4+1 MS program open to all undergraduates, Duke students are aware of the opportunities and rewards in this area as well.

Non-engineers who have the prerequisite courses in programming, linear algebra and introductory statistics will be able to complete the Minor in Machine Learning & Artificial Intelligence, complementing the focus of their primary major while gaining a valuable credential. Of the five technical courses required for the minor, three are fundamental to the discipline: Intermediate Statistics and Probability, Introductory Machine Learning and Artificial Intelligence, and Intermediate Machine Learning and Artificial Intelligence. Two additional upper-level courses provide greater depth of study. 

There is some flexibility in selecting these courses, which allows students to tailor their course of study to their specific interests, said Lisa Huettel, Duke ECE’s director of undergraduate studies, and member of the faculty planning group. “One of the hallmarks of a Duke Engineering education is that students are able to customize their education based on their unique interests,” said Huettel. “The new minor will be a valuable tool that students from any discipline can use to chart their own professional course.”

“The new minor will be a valuable tool that students from any discipline can use to chart their own professional course.”

Lisa HUettel, DIrector of ECE Undergraduate studies

The list of approved electives include courses like Image and Video Processing, Computer Vision and Natural Language Processing.

“Some background in ML will be useful across a wide variety of disciplines,” said Pfister, who cited natural language processing as a prime example. “I first learned to find books in the library by searching the card catalog. Now, because of the way computer databases have revolutionized the storage and search of data, I can find and reserve a library book online. But I still can’t say, ‘Find me a book about rowing where the main character is teenaged girl living in Idaho.’ Recent advances in natural language processing are likely to change that. In fact, any field that could benefit from more intelligent automated searching of texts will likely benefit from machine learning.”

The list of approved electives include courses like Image and Video Processing, Computer Vision and Natural Language Processing.

“Some background in ML will be useful across a wide variety of disciplines,” said Pfister, who cited natural language processing as a prime example. “I first learned to find books in the library by searching the card catalog. Now, because of the way computer databases have revolutionized the storage and search of data, I can find and reserve a library book online. But I still can’t say, ‘Find me a book about rowing where the main character is teenaged girl living in Idaho.’ Recent advances in natural language processing are likely to change that. In fact, any field that could benefit from more intelligent automated searching of texts will likely benefit from machine learning.”

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