Deep Learning Semester Project

In this project you will apply deep learning to some data set. You may use your own data or a data set that I provide.

You may work alone or in a team. If you work in a team, I will expect a little more from your project. You may team up with someone outside the class if you are working on a research problem joutside of this class.

If you are using this class project to work on research outside the class, I strongly recommend that you try to publish your work. I will work with you to identify an appropriate publication venue, design your research methodology, and write your paper.

You will produce the following deliverables, due at appropriate points during the semester (see the schedule):

• Project proposal. Describe your data, where it comes from, what you want to get from the data, and why it's interesting.
• (Intermediate) Results. Based on your stated goals above, report the results (so far) of the deep learning model you developed, using confusion matrices, ROC curves, and any other appropriate performance measures.
• Finished research paper. A finished research paper ready to submit for publication. Of course, you may continue to work on it after this class is over.
• Presentation. A 20-minute conference-style presentation to the class during the last two or three class meetings, depending on how many groups we have.

You will write your proposal, results and finished research paper in $LATEX$ using the style files of the venue to which you intend to submit your work, or NeurIPS style files if you don't (yet) have a target venue. Your code should be written in Python using the PyTorch deep learning framework. You should keep your code, data, and paper up to date in a private GitHub repository to which you grant me access.