CPH 200A
Computational Precision Health Cornerstone A
CPH students will develop skills and expertise in both the computational sciences and health sciences. In this three-part course series, students learn to integrate core foundations of computational precision health in small multidisciplinary teams using a Problem Based Learning pedagogical approach. Students will learn core computational foundations including in machine learning and causal inference and they will obtain deep exposure to multiple clinical areas including cancer and cardiology.
Topics
- Upon completion of this unit students will be able to: Formulate precision health research questions to address challenges in the covered disease areas (e.g. cancer and cardiology) Develop machine learning models to leverage, text, images, volumes and time-series data Develop advanced algorithms for self-supervised learning and generative modeling Understand the fundamentals of causal inference and uncertainty quantification. Develop modular and reusable software implementations of deep learning models and set up reproducible experiments.
Official catalogue course objectives and student learning outcomes
Sources
- Course catalogue ↗2026-2027