CS 3960 - Algorithm Fairness in Machine Learning
Syllabus: Course Critical Information (see Canvas for details)
MoWe / 4:35 p.m. - 5:55 p.m., MEB 2325
Instructor: Bei Wang Phillips
TA: Xinyuan Yan
Instructor Office Hours:
Prof. Bei Wang Phillips: WEB 4608 (see Canvas for the latest schedule); or by appointment.
TA office hours: Xinyuan Yan (see Canvas for the latest schedule); or by appointment.
Course Catalog Description: This course studies how to ensure, via algorithmic developments, that biases in both the data and the model do not lead to models that treat individuals unfavorably on the basis of race, gender, income, etc. The course trains the next generation data scientists who employ, implement, or deploy fairer machine learning tools.
Prerequisites: CS 3190 Foundations of Data Analysis
This class has no required textbook but offers a number of required reading materials, including:
Course Outline (including but not limited to):
Learning objectives: Upon completion of CS 3960, students are able to:
Assignments will be writing or coding assignments that address specific questions based on the course materials. Each writing assignment will generally be no more than 2 pages long (11 pt, single spaced). Each coding assignment will involve writing segments of code in Python or utilizing existing machine learning Python libraries (such as scikit-learn). Each assignment should be turned in electronically (in PDF format, either generated directly or exported from another text editing mechanism).
The project involves a detailed study of algorithm fairness that contains both a writing and a coding component. It will be an algorithm fairness project in a data science context.
Acknowledgement: This course is developed with a grant from the Utah System of Higher Education (USHE) Deep Technology Initiative.