| Instructor | Julian McAuley |
| Room | Peterson Hall, 108 |
| Days & times | 9:30-10:50am, Tuesdays & Thursdays |
| Office hours | posted on Piazza |
[piazza] [gradescope] [twitch] [podcast]
This course is devoted to fairness, bias, and transparency in machine learning. After taking this course, you will be able to understand the main sources of bias and unfairness in machine learning systems, and deploy strategies to mitigate these biases. You will also understand the related notions of accountability and transparency in Machine Learning, allowing for the development of systems that are more trustworthy.
This page is under construction!
| module | week (approx) | slides | (w/annotations) | workbook |
|---|---|---|---|---|
| intro and outline | ||||
| 1 | 1-2 | regression and classification | (w/ annotations) | workbook 1 and datasets |
| 2 | 3-4 | intro to bias and fairness | (w/ annotations) | workbook 2 |
| 3 | 4-5 | fairness and bias interventions | (w/ annotations) | workbook 3 |
| 4 | 6-7 | interpretable and explainable AI | (w/ annotations) | workbook 4 |
| 5 | 8-9 | fairness and bias in application domains | (w/ annotations) | |
| 10 | project presentations? (if time allows) |
| Homework | 50% | Assignments | 50% |
|---|---|---|---|
| ├ Homework 1 | 10% | ├ Assignment 1 | 25% |
| ├ Homework 2 | 10% | ├ Assignment 2 | 25% |
| ├ Homework 3 | 10% | ||
| ├ Homework 4 | 10% | ||
| ├ Homework 5 | 10% |