Advanced Machine Learning for Health and Medicine (BINF 4008 / COMS 4995)
Tentatively offered in the fall semester of every other year.
Machine Learning (ML) has transformative potential for applications in health and medicine. The complexity of healthcare and medicine has highlighted foundational challenges in ML such as lack of generalization, robustness, safety, inequity, and statistical interpretability. Traditional ways of measuring the utility of ML models often do not reflect clinical/medical benefits resulting in technical innovations in ML methods. Exciting opportunities have opened up for methodological progress in Machine Learning motivated by health and medicine applications. The availability of large de-identified multi-institutional datasets for healthcare and medicine from around the world has accelerated progress in ML for health. These data are from healthcare providers and are patient-facing as opposed to traditional medical data, which is well-curated and collected for specific tasks. Patient-facing datasets are rife with some of the most complex statistical artifacts that require innovative ML solutions to improve over the state-of-the-art in health and medicine. In this course, you will learn about complexities that make health and medicine data unique and how it opens up opportunities for advanced AI. You will learn advanced Machine Learning methods useful in health and medicine applications, for example, time-series modeling, reinforcement learning, probabilistic modeling, causal inference, foundation models, unsupervised learning, and self-supervised learning. I will further provide an overview of challenges such as fairness, interpretability, generalization, robustness, safety, and policy implications of ML in health and medicine. The course will train students to map real-world challenges of working with health and medical data to statistical challenges that require new and advanced ML methods.
Prerequisites
- Machine Learning (COMS 4771) or equivalent (BINF 4002) with grade B or higher.
- Familiarity with Python and one or more of: Pytorch, Tensorflow, JAX.
- Basic knowledge of probability, statistics, and linear algebra.
Machine Learning For Healthcare (BINF 4002)
Tentatively offered in the spring semester of every other year.
Further details TBA.