Machine Learning in Health and Biomedical Sciences

Collaborative Specialization



The Collaborative Specialization in Machine Learning in Health and Biomedical Science is an interdisciplinary training field that is designed to enable students apply modern machine learning approaches to improve human health and well-being. Innovations in health and biomedical science are increasingly driven by large sets of genetic, physiological, imaging, and behavioral data, and the algorithms necessary to analyze them.

The specialization provides a solid foundation in machine learning and artificial intelligence techniques, and its application to problems in health and biomedical sciences. Special training is provided in project development, end-user engagement, and a reflected practice that considers the ethical implication of the developed techniques.

Students are required to pass one foundational and at least one applied machine learning course, and to complete a thesis in their home program that is relevant to the field. The core-seminar provides Entrepreneurial, communication, ethical, and project management training that prepares the students for a career in this rapidly growing sector of the Canadian industry.

Program Length

  • Varies depending on a student’s home degree program.

Program Design

  • Full-time study
  • Thesis-based

Admission Requirements

  • Successful admission to a graduate program that is part of the Collaborative Specialization.
  • A proposed thesis topic that is eligible with respect to the Collaborative Specialization is required for admission. The thesis title and topic (in form of a one-page abstract) needs to be submitted to the CS steering committee prior to admission to the collaborative specialization.

English Language Proficiency

  • Requirements of the student’s home degree program need to be met.

Application Deadline

  • Please see the individual home program deadlines.