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Adjunct Instructor: GPH-GU 2338/3338 Machine Learning in Public Health
New York University
📍
New York, United States
Location
New York
Posted
June 20, 2026
Commute
Local Area
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Job Description
Position: Adjunct Instructor
Course: GPH-GU 2338/3338 Machine Learning in Public Health ( Syllabus) (https://drive.google.com/file/d/1iRsXedhJn9f1oDhVNva0U3wiOSflho6q/view?usp=sharing)
Department: NYU School of Global Public Health - Biostatistics
Supervisor: Dr. Rebecca Betensky
Employment Dates: Spring 2026
This course will provide students with a comprehensive understanding of machine learning and its applications in public health and biomedicine. Topics covered include the data generating process, model selection and evaluation, generalized linear models, various supervised and unsupervised machine learning algorithms (such as support vector machines, decision trees, random forests, neural networks, and k-means), and ethical considerations in machine learning.
Students will learn how to implement machine learning methods effectively, including the assessment of assumptions about the data-generating process, the creation of relevant feat...
Course: GPH-GU 2338/3338 Machine Learning in Public Health ( Syllabus) (https://drive.google.com/file/d/1iRsXedhJn9f1oDhVNva0U3wiOSflho6q/view?usp=sharing)
Department: NYU School of Global Public Health - Biostatistics
Supervisor: Dr. Rebecca Betensky
Employment Dates: Spring 2026
This course will provide students with a comprehensive understanding of machine learning and its applications in public health and biomedicine. Topics covered include the data generating process, model selection and evaluation, generalized linear models, various supervised and unsupervised machine learning algorithms (such as support vector machines, decision trees, random forests, neural networks, and k-means), and ethical considerations in machine learning.
Students will learn how to implement machine learning methods effectively, including the assessment of assumptions about the data-generating process, the creation of relevant feat...