Duke Machine Learning Spring School: Health AI
March 9-11, 2026 | In Person at Duke University, Schiciano Auditorium
The Duke AI Health Community of Practice is pleased to announce the Duke Machine Learning Spring School 2026: Health AI (MLSS-HealthAI), offered in March as an in-person three-day class that will provide perspective and insight on the fundamentals of generative artificial intelligence methods and applications.
The curriculum in the MLSS-HealthAI is targeted to individuals interested in learning about generative AI with health applications, and will introduce the mathematics and statistics at the foundation of current generative and representation learning models, plus provide context for the methods that have formed the foundations of rapid growth in artificial intelligence (AI). Additionally, the MLSS-HealthAI will provide hands-on experience during practical sessions that illustrate the use of such methods and applications.
This is the 13th 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 2025, attracted 220 participants.
To register for the MLSS, please visit https://events.duke.edu/mlschool2026
To request consideration for a scholarship, please visit https://duke.qualtrics.com/jfe/form/SV_5hdTTual6ssrkfY
Program Format
Each day of the 3-day class will provide lecture on the mathematics and statistics at the heart of machine learning, case studies of the methods applied to specific application areas, and a practical session for participants to gain hands-on experience.
Monday, March 9 Topic: Natural Language Processing with Large Language Models
Tuesday, March 10 Topic: Information Retrieval with Large Language Models
Wednesday, March 11 Topic: Conversational Systems with Large Language Models
Each day of the MLSS will be arranged as follows (Eastern Time):
- 9:00-10:30am: Introduction to fundamental concepts
- 11:00am-noon: Use case of the methods illustrated by a specific area of application
- 1:00-2:30pm: Practical session for hands-on experience
Program Details: Location, Registration and Cost
Location: In Person at Duke University, Schiciano Auditorium
The registration fee is $275, with a discounted rate for members of nonprofits of $60, and a discounted student rate of $25 payable through the registration site. 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-HealthAI 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-HealthAI 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-HealthAI material more accessible; however, it is not required to benefit from much of the program. Finally, the class will also introduce participants to practical experience in the use of such tools and platforms.
If you have any questions, please send an e-mail to aihealth@duke.edu
Photograph from the 2025 Machine Learning Summer School in the Schiciano Auditorium
