Adjunct Instructor: GPH-GU 2338/3338 Machine Learning in Public Health

Job description

Description Position: Adjunct Instructor: Course: GPH-GU 2338/3338 Machine Learning in Public Health ( Syllabus) 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 features, the handling of missing data, and the reduction of bias. In addition to gaining familiarity with the potential power of machine learning in public health, students will also explore the specific challenges and limitations inherent to these applications. By the end of the course, students will have a solid found...