Machine Learning for Health

Catalog description

This course introduces students the various real-world health related problems such as patient screening, risk modeling, disease subtyping and precision medicine, along with their associated data, such as patient clinical records, medical images, physiological and vital signals from wearable sensors, multi-omics, etc. and how to use appropriate machine learning algorithms to analyze these data and help with the corresponding real-world health problems. The machine learning techniques involved in this class include classic supervised and unsupervised learning, network analysis, probabilistic modeling, deep learning, transfer learning, federated learning, algorithmic fairness and interpretability. We will also invite clinicians or researchers working in the health industry to deliver guest lecturers in the class. The students will gain hands-on experience on analyzing real world health data during course assignments and projects.