DATAC146
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Foundations for Computational Precision Health
Course Title
Foundations for Computational Precision Health
Course Description
Students will build expertise in developing machine-learning tools to address challenges in health care. The course emphasizes both “how to formulate useful computational health problems”, and “how to develop computational solutions”. On the health side, we’ll get clinical guest lectures exploring challenges across diverse areas of healthcare (e.g., cardiology, cancer, primary care). On the computational side, the course will cover machine learning and deep learning foundations, state-of-the-art neural networks, and then advanced research topics. The course will emphasize rigorous evaluation, algorithmic bias, deployment, and auditing. The class will culminate in an open-ended final project, integrating skills learned in the course.
Minimum
3
Maximum
3
Grading Basis
Default Letter Grade; P/NP Option
Method of Assessment
Alternative Final Assessment
Instructors
Yala, Chen
American Cultures Requirement
No
Reading and Composition Requirement
None of the Reading and Composition Requirement
Prerequisite
Complete ALL of the following Courses:
Repeat Rules
Course is not repeatable for credit.
Course Objectives
Cross-Listed Course(s)
Formats
Lecture
Term
Fall
Duration (in weeks)
15
Minimum Hours
2
Maximum Hours
2
Lecture Mode of Instruction
In Person
Outside Work Hours Min
7
Maximum Hours
7