Collaborative Data Science for Healthcare (HST.953)
A guide for students who are interested in performing retrospective research using data from electronic health records (Medical Information Mart for Intensive Care or MIMIC database and the eICU Collaborative Research Database). Covers steps in parsing a clinical question into a study design and methodology for data analysis and interpretation, but the emphasis is on the data curation that is required before any analysis can be performed. Activities include review of case studies using the MIMIC and the eICU CRD, and a team project. Student teams choose a question and clinician to work with for their project. Teams meet weekly with clinicians at the hospitals at pre-arranged time.
Ethical Machine Learning in Human Deployments (6.882)
This course focuses on the human-facing considerations in the pipeline of machine learning (ML) development in human-facing settings like healthcare, employment, and education. Students will learn about the issues involved in ethical machine learning, including framing ethics of ML in healthcare through the lens of social justice. Students will read papers related to ongoing efforts and challenges in ethical ML, ranging from problem selection to post-deployment considerations. Guest lectures will be given by experts in data access, ethics, fairness, privacy and deployments, and the course will focus around a central project that students will use to explore how machine learning can potentially be brought into human-facing deployments ethically.
Machine Learning (CSC 2515 @ UofT)
Machine learning (ML) is a set of techniques that allow computers to learn from data and experience, rather than requiring humans to specify the desired behaviour manually. This course introduces the main concepts and ideas in ML, and provides an overview of many commonly used machine learning algorithms. It also serves as a foundation for more advanced ML courses.
The students will learn about ML problems (supervised, unsupervised, and reinforcement learning), models (linear and nonlinear, including neural networks), loss functions (squared error, cross entropy, hinge, exponential), bias and variance tradeoff, ensemble methods (bagging and boosting), optimization techniques in ML, probabilistic viewpoint of ML, etc.
Machine Learning for Health (CSC 2547 @ UofT)
This course will give a broad overview of machine learning for health. We begin with an overview of what makes healthcare unique, and then explore machine learning methods for clinical and healthcare applications through recent papers. We discuss the recent successes of of graphical models, deep learning, time-series analysis, and transfer learning in the context of health. We also broadly cover concepts of learning, algorithmic fairness, interpretability, and causality. We emphasize the importance of collaboration between technical and non-technical researchers, and consider the implications of machine learning in healthcare governance and policy. Students will choose and complete a course project, and make project presentations at the end of the course.