Location: Learning Hall of the Trent Semans Center
Together Duke, with the Office of the Provost, Department of Pediatrics and +DS, are pleased to announce the Machine Learning School for the School of Medicine (MLS-SOM), being offered for the first time in March 2019, to introduce Duke University School of Medicine faculty and staff to the machine learning and deep learning techniques poised to disrupt clinical practice. The MLS-SOM will present foundational material and clinical case studies in four specific areas of machine learning with established clinical applications and impact, and content will be tailored to a clinical audience. The MLS-SOM will be offered as both an evening lecture series and a two-part afternoon course, and participants may select the options best suited to their schedule.
Material will be divided into four blocks, each focused on an area of machine learning highly applicable in healthcare settings. All blocks will be two hours in length. The first portion of each block (approx. 75 minutes) will provide an intuitive, accessible introduction to foundational material and concepts. Visual explanations will be favored over mathematical formulas wherever possible. The second portion (approx. 45 minutes) will present a detailed case study from the clinical literature that illustrates how concepts and methods can be effectively applied to a clinical problem. Knowledge of mathematics may be helpful but is not required, and programming experience is not required.
Program Format A: Evening Lecture Series
Content blocks will be delivered as individual two-hour lectures on four consecutive Mondays beginning on March 11th (3/11, 3/18, 3/25, 4/1). Lectures will take place from 6:00 pm to 8:00 pm with an informal dinner provided.
Program Format B: Afternoon Course in Two Parts
Content will be delivered over two afternoons in March (3/13 and 3/27) from 12:00 pm to 4:00 pm, with an informal lunch provided and a short break at approximately 2:00 pm. Blocks 1 and 2 will be covered on March 13th, and blocks 3 and 4 will be covered on March 27th.