Machine learning, broadly defined as analytic techniques that fit models algorithmically by adapting to patterns in data, is growing in use within epidemiology. This workshop will explore how epidemiologists can use machine learning to advance their research and practice, while reflecting on some of the ethical and scientific considerations that arise from the use of data-driven techniques. The workshop will use a flipped classroom format to maximize time for discussion and programming activities during the SER workshop. Prior to the workshop, attendees will be sent 2-3 readings and links to 2-3 30 minute videos. These videos will introduce key terms, commonly-used algorithms, evaluation techniques and examples of epidemiologic studies that incorporated machine learning. During the workshop, these topics will be reinforced through a review of concepts, guided discussions, presentations of case-studies and demonstrations of analytic pipelines using R/R Studio. Attendees will work individually and in small groups on hands-on programming exercises of publicly available data, while also discussing the ethical and scientific challenges presented by different research scenarios. At the conclusion of this workshop, attendees will be able to discuss scenarios where machine learning can benefit epidemiologic analysis, analyze public health data using commonly-used algorithms, and feel empowered to pursue additional training or collaborate with scientists with expertise in machine learning.
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