Earlier this June, the Duke Machine Learning Summer School 2025: Generative AI (MLSS-GenAI) concluded its five-day series of classes focused on the fundamentals of generative artificial intelligence methods and applications. Sponsored by the Duke AI Health Community of Practice, the MLSS-GenAI class was led by AI Health Faculty Council Member Ricardo Henao, PhD, an associate professor in the Department of Biostatistics and Bioinformatics in the Duke University School of Medicine. Other seminar teachers included an array of Duke faculty representing biostatistics, engineering, medicine, and biology, as well as former and current Duke AI Health Faculty Affiliates.

In addition to offering context for the methods underpinning the applications that have prompted such rapid growth in AI tools, the MLSS-GenAI also offered hands-on training in the latest machine learning software, including widely used free PyTorch framework. As with previous Machine Learning Summer School sessions, the MLSS-GenAI incorporated rigorous instruction in mathematics and statistics underlying current AI models. However, the course was designed to accommodate learners interested in generative AI and deep learning methods regardless of background, and each day’s classes included “mathematically light” portions as well as hands-on coding sessions and case studies.

This session marked the 12th iteration of the Duke Machine Learning School, which has been presented since 2017 in both summer and winter offerings. On hiatus since 2022, the 2025 MLSS GenAI summer school rapidly filled all available seats, and included a lecture co-presented by Duke AI Health Founding Director Larry Carin, PhD.

This series has reached hundreds of participants from academia and industry, including international audiences at the SingHealth/Duke NUS Medical School and the Duke Kunshan University campus. The last machine summer school held in 2022 attracted 130 participants from around the world, representing 41 universities, institutes, and corporations.