DATA622
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Machine Learning for Health Data Science
Graduate Science Education (O)
MD - Cumming School of Medicine
Subject
DATA - Data Science
Description
An introduction to the application of machine learning methods to problems in health data. The concepts of precision medicine and precision public health are introduced and the role of data science in these endeavors is explored. Using real examples from health data, various contemporary machine learning techniques are taught.
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Health Data Science and Biostatistics.
Antirequisite(s): Credit for Data Science 622 and Community Health Sciences 615 will not be allowed.
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Health Data Science and Biostatistics.
Antirequisite(s): Credit for Data Science 622 and Community Health Sciences 615 will not be allowed.
Course Attributes
Fee Rate Group(Domestic) - H, Fee Rate Group(International) -G, GFC Hours (3-0)
Courses may consist of a Lecture, Lab, Tutorial, and/or Seminar. Students will be required to register in each component that is required for the course as indicated in the schedule of classes. Practicums, internships or other experiential learning modalities are typically indicated as a Lab component.
Component
LEC
Units
3
Repeat for Credit
No
Subject code
DATA
Understanding Course Information
Please refer to Course Terminology and Description to better understand how to interpret course information such as GFC Hours, pre-requisites, course levels, etc.