Exposure Modeling
Master exposure modeling in a two-day Boot Camp, applying traditional and machine learning methods to predict environmental variations using real datasets.
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Course Description
The Exposure Modeling Boot Camp is a two-day workshop focused on skills development in the application of both traditional and machine learning methods in predicting spatial/temporal variations in environmental exposures (e.g., air pollution, temperature, noise) using real data sets.
- Understand the application of exposure science in environmental epidemiology, learning key concepts for modeling environmental exposures' impact on health.
- Gain intuition on traditional methods such as linear regression and generalized additive models, and modern machine learning techniques like random forest and neural networks.
- Develop skills in handling and cleaning environmental exposure datasets, ensuring accuracy and reliability for modeling.
- Learn how to develop, assess, and refine predictive models for environmental exposures, utilizing both traditional and non-traditional data streams like images and audio.
To contact support for this course, please email [email protected].
Course Prerequisites
- Introductory background in statistics (i.e., linear and logistic regression).
- Familiarity with R/RStudio.
- Familiarity with Python is an asset but is not required.
- Each participant is required to have a personal laptop and a free, basic Posit Cloud (formerly RStudio Cloud) account. All lab sessions on the first day will be done using Posit Cloud (formerly RStudio Cloud).
- Some lab sessions will use Google CoLab, so each participant is required to have a Google CoLab account (you will need a Google account to access Google CoLab).
What You Will Learn
This two-day workshop is focused on practical skills development in modeling environmental exposures using both traditional and machine learning methods. The workshop is led by Dr. Scott Weichenthal (Associate Professor, McGill University) who has extensive experience in the development and application of exposure models in environmental epidemiology. Morning sessions will include lectures discussing important concepts related to exposure science and exposure modeling in environmental epidemiology and afternoon sessions will focus on hands-on laboratory exercises applying both traditional (e.g., linear regression, generalized additive models) and machine learning methods (e.g., random forest, neural networks) in modeling environmental exposures using real data sets. Participants will learn practical skills in working with environmental exposure data and will gain knowledge in the application of multiple approaches to modeling environmental exposures known to impact human health.
By the end of the workshop, participants will be familiar with the following topics:
- Principles of exposure science as applied to environmental epidemiology
- The intuition behind how various modeling approaches work including linear regression models, generalized additive models, random forest models, dense neural networks, and convolutional neural networks
- Data handling and cleaning
- Developing and evaluating predictive models
- Data collection and management for exposure models based on non-traditional data streams including images and audio data
Instructors
Dr. Weichenthal is an Associate Professor in the Department of Epidemiology, Biostatistics, and Occupational Health. His research program is dedicated to identifying and evaluating environmental risk factors for chronic diseases such as cancer and cardiovascular disease.