Overview. The practice of Machine Learning (ML) increasingly involves making choices that impact real people and society at large. This course covers an array of ethical, societal, and policy considerations in applying ML tools to high-stakes domains, such as employment, education, lending, criminal justice, medicine, and beyond. We will discuss:
Prerequisite knowledge. Familiarity with standard Machine Learning concepts is a prerequisite. In particular, students are expected to have adequate knowledge of probability, statistics, linear algebra, and optimization as it pertains to introductory ML topics. Basic coding skills is a requirement. Some background in algorithm design, mechanism design and game theory will be helpful but not necessary.
Performance Assessment. The final grade will be computed based on:
For a more detailed syllabus, please view this document. (Note: you need to be signed in via your CMU credentials to be able to access the document).
Please visit to course syllabus for statements regarding wellness, diversity, disability accommodations, and academic integrity. In the same document, you can find course policies regarding classroom expectations, attendance and participation, recording, and late/make-up work.
We will be using Canvas for future announcements, assignments, and publishing of the course material. This page will not be updated frequently.