This course aims to provide an introduction to topics and bias and fairness in machine learning (ML), with a particular emphasis on applications in healthcare. You will write a tutorial paper for an informatics audience on the topic. You will be responsible for doing the literature review, writing the paper, running data analysis, and making all code available on GitHub.
| Week | Assignment |
|---|---|
| 1 | UTOPIAN Data Access; Simulation Study Design |
| 2 | Simulation Study Design; Edit Draft |
| 3 | Simulation Study; Write Intro |
| 4 | Simulation Study; Supplementary Materials |
| 5 | Simulation Study; Supplementary Materials |
| 6 | Simulation Study; Edit Draft |
| 7 | READING WEEK |
| 8 | Run Real Data Analysis |
| 9 | Run Real Data Analysis |
| 10 | Run Real Data Analysis; Edit Draft |
| 11 | Edit Draft |
| 12 | Edit Draft |
| 13 | Submit Manuscript |
- Weekly Manuscript Check: 35%
- Simulation Studies: 30% (Feb 15)
- Real Data Analysis: 40% (March 30)
This course aims to provide an introduction to topics and bias and fairness in machine learning (ML), with a particular emphasis on applications in healthcare. As a starting point, we will do a thorough review of the literature. We will then write a tutorial paper for an informatics audience on the topic. You will be responsible for doing the literature review, writing the paper, running data analysis, and making all code available on GitHub.
| Week | Assignment |
|---|---|
| 1 | bias in real life; early fairness paper |
| 2 | overview paper; overview paper |
| 3 | overview paper; overview paper - See references therein |
| 4 | comparative analysis; Outline introduction |
| 5 | Finish literature review summary; Outline paper |
| 6 | Outline paper |
| 7 | READING WEEK |
| 8 | Outline paper |
| 9 | Draft methods section |
| 10 | Draft methods section; Run analysis |
| 11 | Run analysis |
| 12 | Draft analysis section |
| 13 | Write introduction and discussion |
- Literature review: 30% (Oct 15)
- Data analysis: 30% (Dec 15)
- Manuscript draft: 40% (Dec 15)