Duke Machine Learning Summer School: Generative AI
June 2–6, 2025 | In Person at Duke University, Schiciano Auditorium
The Duke AI Health Community of Practice is pleased to announce the Duke Machine Learning Summer School 2025: Generative AI (MLSS-GenAI), offered in June as a (presential) five-day class that provides lectures on the fundamentals of generative artificial intelligence methods and applications. The curriculum in the MLSS-GenAI is targeted to individuals interested in learning about generative AI, with a focus on recent deep learning methodologies.
The MLSS-GenAI will introduce the mathematics and statistics at the foundation of current generative and representation learning models and provide context for the methods that have formed the foundations of rapid growth in artificial intelligence (AI). Additionally, the MLSS-GenAI will provide hands-on training in the latest machine learning software, using the widely used (and free) PyTorch framework.
This is the 12th iteration of the Duke Machine Learning School presented since 2017. 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.
To register for the MLSS, please visit https://events.duke.edu/mlss2025
To request consideration for a scholarship, please visit https://duke.qualtrics.com/jfe/form/SV_09eREHD4BXNGXVI
Program Format
The 5-day class will provide lectures on the mathematics and statistics at the heart of machine learning, plus hands-on training on implementing machine learning tools with the PyTorch software platform, and case studies of the methods applied to specific application areas.
Day 1: Basics of Generative Models with David Carlson, PhD and Helen Li, PhD
Day 2: GenAI for Text with Larry Carin, PhD and Buwan Dhingra, PhD
Day 3: GenAI for Images with Ricardo Henao, PhD and Tim Dunn, PhD
Day 4: GenAI for Following Instructions with Monica Agrawal, PhD and Shuyan Zhou, PhD
Day 5: GenAI for Biological Sequences with Rohit Singh, PhD
Each day of the MLSS will be arranged as follows (Eastern Time):
- 9:00-10:15am, Lecture 1: Mathematically-light introduction to the focus of the day
- 10:45am-noon, Lecture 2: Mathematically rigorous discussion of the focus of the day
- Afternoons beginning at 1:00pm: Coding sessions and case studies
Program Details: Location, Registration and Cost
The registration fee is $400, with a discounted rate for members of nonprofits of $150, and a discounted student rate of $50 payable through the registration site. Registration is free for Duke students, faculty, and staff. All fees are non-refundable.
Once we reach maximum registration, we will maintain a waitlist, and will contact those on the waitlist as spots become available.
We also have a small number of scholarships available for those who would be otherwise unable to join.
Who Should Attend
The MLSS-GenAI is particularly well-suited to members of academia and industry, including students and trainees, who seek a thorough introduction to the methods of generative AI, including interpretation and commentary by respected leaders in the field.
The MLSS-GenAI is meant to provide value to students at multiple levels of mathematical sophistication (including with limited such background). On each day, an initial emphasis will be placed on presenting the concepts as intuitively as possible, with minimum math and technical details. As the concepts are developed further, more math will be introduced, but only the minimum necessary to explain the concepts. Then case studies will show how the technology is used in practical generative AI applications, and these discussions should be accessible to most students (concepts emphasized over detailed math). Strength in mathematics and statistics is a significant plus and will make all MLSS-GenAI material more accessible; however, it is not required to benefit from much of the program. Finally, the class will also introduce participants to the coding software used to make such technology work in practice.
If you have any questions, please send an e-mail to aihealth@duke.edu
Photograph from the 2019 Machine Learning Summer School in the Schiciano Auditorium
