Artificial Intelligence in Environmental and Global Health

3 Credits

Discusses fundamental principles, methodology and applications of machine learning and artificial intelligence approaches in environmental and global health, including physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship (QSAR) modeling for toxicity prediction, air pollution, water pollution, human biomonitoring, infectious disease, antimicrobial resistance, and big data.