Previous Versions:
- All content will be on github in this repo including schedule and tech setup instructions
- All assignments will be on and submitted through canvas
- Class communication and announcements will be primarily through Slack
This is a project-based course designed to provide students training and experience in solving real-world problems using machine learning, exploring the interface between research and practice, with a particular focus on topics in fairness and explainability.
The goal of this course is to give students exposure to the nuance of applying machine learning to the real-world, where common assumptions (like iid and stationarity) break down, and the growing needs for (and limitations of) approaches to improve fairness and explainability of these applications. Through project assignments, lectures, discussions, and readings, students will learn about and experience building machine learning systems for real-world problems and data, as well as applying and evaluating the utility of proposed methods for enhancing the interpretability and fairness of machine learning models. Through the course, students will develop skills in problem formulation, working with messy data, making ML design choices appropriate for the problem at hand, model selection, model interpretability, understanding and mitigating algorithmic bias & disparities, and evaluating the impact of deployed models.
| Rayid Ghani | Kit Rodolfa |
|---|---|
 GHC 8023 Office Hours: TBD |
 GHC 8018 Office Hours: TBD |
| Riyaz Panjwani | Abhishek Parikh |
|---|---|
 Office Hours: TBD |
Photo Office Hours: TBD |
Note that this course is being offered pass/fail.
Weekly project update assignments (10%)
Midterm take-home exam (20%)
Write-up on interpretability findings (15%)
Write-up on fairness findings (15%)
Group presentation (5%)
Future research or project proposal (15%)
Quizzes on readings and concepts (5%)
Class attendance and participation (10%)
Submitting weekly check-in and feedback forms (5%)
See the syllabus for much more detail as well, including information about group projects, grading, and helpful optional readings.