MDCH615
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(DATA624)Machine Learning for Health Data Science
Subject
MDCH - Community Health Sciences
Description
An introduction to machine learning with a focus on health applications. While the theoretical foundation behind each machine learning method will be covered, emphasis will be on hands-on skills and practical applications. Individual work on in-class quizzes and computer assignments to learn basic knowledge and hands-on skills as well as interactive class discussions and teamwork.
Prerequisite(s): Data Science 603, 604 and admission to the Graduate Diploma in Data Science and Analytics; or proficiency in Python programming and successful completion of at least one mathematics course at either the undergraduate or graduate level and consent of the program.
Antirequisite(s): Credit for Community Health Sciences 615 and Data Science 622 will not be allowed.
Prerequisite(s): Data Science 603, 604 and admission to the Graduate Diploma in Data Science and Analytics; or proficiency in Python programming and successful completion of at least one mathematics course at either the undergraduate or graduate level and consent of the program.
Antirequisite(s): Credit for Community Health Sciences 615 and Data Science 622 will not be allowed.
Course Attributes
Fee Rate Group(Domestic) - A, Fee Rate Group(International) -B, GFC Hours (3-0), RCS Related, FRGD - A (Fee Rate Group(Domestic) - A), FRGI - B (Fee Rate Group(Int'l) - B), GFCH - 3-0 ((3-0)), RCS - RLT (Related)
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
MDCH