https://github.com/pdwaggoner/proposal

Submit your assignment here (see workflow in the syllabus for help)
https://github.com/pdwaggoner/proposal

Last synced: 24 days ago
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Submit your assignment here (see workflow in the syllabus for help)

• Host: GitHub
• URL: https://github.com/pdwaggoner/proposal
• Owner: pdwaggoner
• Created: 2019-09-04T16:47:47.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-09-04T17:00:57.000Z (about 5 years ago)
• Last Synced: 2023-09-11T23:10:33.020Z (about 1 year ago)
• Size: 4.88 KB
• Stars: 0
• Watchers: 1
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Proposal

**Project Proposal** (*10%*): The proposal is where your group will (unsurprisingly) pitch its project idea. The goal of this stage is two-fold. First, it is a good first, yet relatively low-stakes test of your group dynamics. As this is where you will introduce the problem, methods/solution, data, and so on, such a task reuqires consolidation and universal contribution to the overall vision of the project; in a word, *teamwork*. And second, the goal of the proposal is to get early-stage feedback from myself and the TA(s) on the feasibility of the project, both regarding your ability to complete it within the time frame of the course, as well as whether your project is in the scope of the class.

	

There are relatively few guidelines for the subject matter of your final project. My only requirement is that the methods at a minimum *incorporate* unsupervised machine learning techniques covered in the course. For example, you could use a clustering technique to label (previously unlabeled) data, which you could then feed to a supervised classifer to predict future classes. Or alternatively, your entire project could be an unsupervised project where, e.g., you may be interested in mapping and categorizing political party affiliation in the United States based on responses to survey questions about political preferences, requiring both clustering (e.g., k-means) and latent variable methods (e.g., PCA). Regardless of the direction you choose, the key here is the inclusion of some *unsupervised* component.

	

**You will be working in a team of 2-3 students, which you will organize on your own. Groups must be organized by the end of the first week of class (Friday, October 4).** *Note:* If you are having trouble finding a group, reach out to me sooner rather than later to request help and I will assign you to a group; if I don't hear from anyone, I will assume all students are in groups. As time will go very quickly, you are strongly encouraged to **keep in regular communication with your group members**, whether in person, virtually (e.g., Slack, email, text, etc.), or both throughout the Quarter. If you have any concerns working with someone in your group, please talk to me or a TA for accommodations.

	

*Some particulars*: 2-6 pages double-spaced, excluding references; it is recommended (not required) that you use figures to illustrate technical concepts. Final submissions should be as a PDF document generated from your Markdown (or Jupyter Notebook) document. Use 11- or 12-point standard font, 1-inch margins, and a properly formatted reference list. You can select any reference style you wish (Chicago, APA, MLA, etc.); just be correct and consistent. **_Note_: team proposals are due: Thursday, October 17, by 5 pm.**