Advanced Machine Learning for Health and Medicine (BINF 4008 / COMS 4995)
Tentatively offered in the spring semester of every 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. The generative AI revolution has brought quite a few AI applications to fruition, but many challenges still remain. The availability of large scale data and the foundation model paradigm brings in exciting opportunities for methodological progress in Machine Learning/Aritficial Intelligence, including generative AI motivated by health and medicine applications with applications that impact patients, clinicians, researchers, regularatory bodies, and data scientists developing such tools. 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 ML/AI methods, for example, time-series modeling, (offline) reinforcement learning, probabilistic modeling, observational causal inference, and how foundation models are being used in medicine, and what challenges remain to be addressed, such as reasoning. 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.
Machine Learning (ML) has transformative potential for applications in health and medicine. You will learn fundamentals of machine learning, deep learning, and modeling clinical and biomedicine data using machine learning.
By the end of this course you will be able to:
- Learn foundations of major machine learning concepts and their relevance to health and biomedicine data
- Develop new ML methods focused on health/biomedicine tasks
- Be better equipped to evaluate and validate ML in healthcare and medicine
Prerequisites
- Basic knowledge of probability, statistics, and linear algebra is expected and required.
- Familiarity with programming with Python and versioning systems like Git is required.