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Duke Machine Learning School Successfully Concludes Summer 2021 Virtual Offering

Duke+Data Science (+DS) recently concluded the 2021 Machine Learning Virtual Summer School (MLvSS). This event, which was the ninth machine learning school held since 2017, was sold out more than a month in advance and completely filled a 100-person waitlist. This high demand demonstrates the continued interest in high-quality instruction by Duke experts in the mathematics and statistics at the foundation of modern machine learning, and context for the methods that have formed the foundations of rapid growth in artificial intelligence.

The MLvSS, held in a live, virtual format June 14-17, 2021, attracted 170 participants from across the world, both students and professionals, representing 43 universities, institutes, and corporations. The full course description is available at

“You covered an amazing amount of depth and breadth in a short time so students could actually apply what they learned when they left,” wrote one participant. Another commented, “My understanding of those concepts and techniques has substantially improved, to the level that I can hope to be able to consider how to use those in my own research program.”

“We were very pleased by the active engagement of the participants in all components of the MLvSS,” said Ricardo Henao, PhD, associate director of AI Health and assistant professor in Biostatistics & Bioinformatics and Electrical & Computer Engineering. “There was excellent engagement in the lectures, coding sessions, and case studies, as well as a positive response to the diversity of topics covered going from the basic concepts of machine learning to applications in digital health, and ethical and societal implications of machine learning. We appreciate the instructors who shared their expertise and insight with this broad audience.”

The 3.5 day curriculum in the Machine Learning Virtual Summer School was designed to provide value to students at multiple levels of mathematical sophistication, including those with limited such background. Each day began with an initial emphasis on presenting the concepts as intuitively as possible, with minimum math and technical details. As the concepts are developed further, more math is introduced, but only the minimum necessary to explain the concepts. Then, case studies show how the technology is used in practice, and these discussions should be accessible to most students (concepts emphasized over detailed math). Finally, the Machine Learning School also introduces participants to the coding software used to make such technology work in practice.

Duke+DataScience (+DS) is a Duke-wide educational initiative devoted to expanding knowledge of and facility with machine learning and other artificial intelligence tools across multiple domains. Since its launch, +DS has reached more than 149,000 learners with the Coursera course “Introduction to Machine Learning,” held 117 short-topic learning experiences, convened 9 multi-day machine learning schools, and the +DS advanced projects program has supported 76 students in substantive applied projects.