HW3: Project Proposal and Outline

Resources for project ideas:

• a list of ideas can be found in Files > project (these projects are being proposed for the MIMIC dataset, see bottom of Course Home page for details/contact us early for permissions access)
• Nightingale is willing to sponsor up to $250 of compute for this dataset: https://docs.ngsci.org/datasets/ed-bwh-ecg Links to an external site. If you'd like to use this, please contact us early for compute/permissions/setup!
• The ACLU of Massachusetts has data sets related to criminal justice, public spending, and more. Each dataset has a catalogued corresponding public records request (if applicable), and you can download the data files for your own analysis. (Site: https://data.aclum.org/, Data: https://data.aclum.org/data Links to an external site.)

Submit a "Project Proposal and Outline" in the format that you would submit a final paper to, e.g., ACM FacCT, NeurIPS, MIT SPR. Your proposal should be 4-8 pages and include the following sections:

A) Introduction

1. Problem statement: What problem does this paper solve? (Or, what question does it answer?)
2. Motivation: Why is this an important or hard problem?
3. Background: What related or previous work is relevant to the project? Include:
   1. The broad consensus and/or dissension in the area,
   2. The open problem(s), and
   3. The gap in the literature.
4. Tiny Sell: What will your work in this project add to the Background in order to address the Problem Statement?

Resource for writing an introduction:
EECS CommKit Links to an external site.

Resources for writing a literature review:
https://writingcenter.unc.edu/tips-and-tools/literature-reviews/ Links to an external site.
https://owl.purdue.edu/owl/research_and_citation/conducting_research/writing_a_literature_review.html Links to an external site. 
https://writingcenter.uagc.edu/writing-literature-review Links to an external site. 

B) Research Proposal

1. Data: What data does this project use to address the Problem outlined above?
2. Sourcing/Labeling: How was this data collected/labeled, and what considerations does that bring to your project?
3. Approach: How will you approach the problem with Machine Learning methods/tools/systems?
   
4. Contributions: What are the new contributions of this paper?

C) Intended Audience:

1. Community: Who is the intended audience?
2. Target: What is the target venue?

D) Timeline:

1. Projection: A week by week break down of milestones for the project.?
2. Break Points: Are there any potential failure points for this project? Note: It is to your advantage to disclose these so that we can help address them, or be aware of them for future grading.
3. Resources: List any help you need with resources.

E) References