Steam-powered Turing Machine University of Washington Department of Computer Science & Engineering
 CSE454 Project Specifications
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Please submit the URL of your proposal to your TA, Sandra, via email by 10/12/2010 10:30am.

The ideal proposal would:

  1. Be written in HTML and delivered as a URL which I could link to on the class site. You can then update it as time progresses.

  2. List the group name and team members.
  3. State clearly the problem you are addressing and what you hope to create as an artifact. Technically, you could satisfy this requirement with a single sentence (and some groups did with the preproposal), but this time I'm looking for much greater detail. If your project has a big UI component, can you include a figure showing what it might look like? Can you include an architectural diagram? The more detail the better, and of course you can change your minds later.

  4. Break the project's development into (much) smaller chunks. Describe each chunk thoroughly. What dependencies are there between the pieces? Can you connect the overall project as a linked schedule or pert chart? Estimate the estimate the time required to develop each chunk and sum up the total time along critical paths. Who will work on each chunk?

    Most likely, these plans will change as time evolves, but it is a very useful exercise to plan it out anyway.

  5. Describe the series of milestones you expect to pass as a way of us each being able to monitor progress at roughly 2.5 week intervals. In order to be concrete, I suggest that you use the following dates for your milestones:
    • Oct 29 - milestone 1 due
    • Nov 17 - milestone 2 due
    • Dec 3 - code complete; start experiments and writing
    • Dec 14 - no final exam; instead we will use the exam period for oral presentations by all the groups, discussing what they accomplished. Written reports and code are due electronically before class with a stapled paper version of the report brought to the class.

  6. If machine learning is involved in your project, describe where you'll get your training (and testing) data

  7. Explain how you will evaluate success. What will you measure, how will you measure it, how will you display it in the final report? (graphs are usually better than tables if you can think of a good way to visualize the data)


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