There are 10 programming projects in this course, plus a final project.
These projects are all hosted on Github at https://github.com/wingated/cs474_labs
This page describes the final project.
To creatively apply knowledge gained through the course of the semester to a substantial learning problem of your own choosing.
There are three deliverables for the project:
A brief description of the problem you have tackled, the data you used, your technical approach and your results (no more than 2 pages) An accounting that shows the total amount of time you spent on your final, broken down by day (no more than one page)
Your final project counts for about 25% of your overall grade (please see LearningSuite for the exact amount).
Your project will be graded as a package. Grading is, by nature, subjective, but it will be primarily weighted by the number of hours you spent on the project. However, both parts of the final are required (ie, do not think that you can log your hours, but not turn in a final report - you will not get credit for your work!).
For the number of hours, I will take the total number of hours you report and divide by 30, then multiply by 100 (and cap at 100%). This will be your rough grade percentage. (So, 30 hours == 100%, 15 hours == 50%, etc.)
I will evaluate your writeup primarily based on the quality of your writing, although I reserve the right to assign some points based on the quality of your project.
Note that no late submissions are possible for this project, because it is done in lieu of the final exam.
For your final project, you should execute a substantial project of your own choosing. You will turn in a single writeup (in PDF format only, please!). Your writeup can be structured in whatever way makes sense for your project, but see below for some possible outlines. It must be polished, well-written and generally of high quality.
Your project will be graded more on effort than results. As I have stated in class, I would rather have you swing for the fences and miss, than take on a simple, safe project. It is therefore very important that your final time log clearly convey the scope of your efforts.
I am expecting some serious effort on this project, so I am expecting that your writeup, even if it's short, reflects that.
For the time log, you must document the time you spent (on a daily basis) along with a simple description of your activities during that time. If you do not document your time, it will not count. In other words, it is not acceptable to claim that you spent 35 hours on your project, without a time log to back it up. I will not accept any excuses about this requirement.
So, for example, a time log might look like the following:
Your writeup serves to inform me about what you did, and simply needs to describe what you did for your project. You should describe:
It should be about 2 pages.
Many different kinds of final projects are possible. A few examples include:
The project can involve any application area, but the core challenge must be tackled using some sort of deep learning.
The best projects involve a new, substantive idea and novel dataset. It may also be acceptable to use vanilla DNN techniques on a novel dataset, as long as you demonstrate significant effort in the “science” of the project – evaluating results, exploring topologies, thinking hard about how to train, and careful test/training evaluation. It may also be acceptable to simply implement a state-of-the-art method from the literature, but clear such projects with me first.
You are welcome to use any publicly available code on the internet to help you.
Here are some possible questions that you might consider answering as part of your report:
Kaggle (current and past)
BYU CS478 datasets
BYU DSC competition