Duke Machine Learning Summer School (MLSS) 2022

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We are pleased to announce the Duke Machine Learning Summer School (MLSS) 2022, offered June 6–10, 2022 as a live five-day class that provides lectures on the fundamentals of machine learning.

The curriculum in the MLSS is targeted to individuals interested in learning about machine learning, with a focus on recent deep learning methodology. The MLSS will introduce the mathematics and statistics at the foundation of modern machine learning, and provide context for the methods that have formed the foundations of rapid growth in artificial intelligence. Additionally, the MLSS will provide hands-on training in the latest machine learning software, using the widely used (and free) PyTorch framework.

To register for the MLSS, please visit https://events.duke.edu/mlss2022 

This is the 11th Duke Machine Learning School presented since 2017. This series has reached hundreds of participants from academia and industry and including international audiences at the SingHealth/Duke NUS Medical School and the Duke Kunshan University campus.

The expert lecturers will include Akshay Bareja, PhD; Alberto Bartesaghi, PhD; David Carlson, PhD; J. Matias Di Martino, PhD; Jessilyn Dunn, PhD; Tim Dunn, PhD; Sina Farsiu, PhD; Zhe Gan, PhD; Ricardo Henao, PhD; Hai Li, PhD; Sarah Rispin Sedlak, JD; and Shashank Srivastava, PhD.

Who Should Attend

The MLSS is particularly well-suited to members of academia and industry, including students and trainees, who seek a thorough introduction to the methods of machine learning, including interpretation and commentary by respected leaders in the field. The MLSS 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 computer vision 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 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.

Program Details: Location, Registration and Cost

Students (with a valid ID, at Duke or other universities) will pay a course fee of $150; the fee for non-students is $400, 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.

To request consideration for a scholarship, please visit https://duke.qualtrics.com/jfe/form/SV_3DHqTxScoyamsJw

Attendees will be able to choose their participation with 2 different options:

• In-person attendance on Duke University’s campus in Durham, North Carolina in the Duke Engineering Wilkinson Building

• Virtual attendance via Zoom

There will be no difference in cost for participants who attend in person vs. participants who attend virtually. This will allow maximum flexibility and personal choice for attendance options.

To learn more, the full course description is available at https://aihealth.duke.edu/mlss2022/