COMPSCI 682 Neural Networks: A Modern Introduction


These project guidelines originally accompany the Stanford CS class CS231n, and are now provided here for the UMass class COMPSCI 682 with minor changes reflecting our course contents. Many thanks to Fei-Fei Li and Andrej Karpathy for graciously letting us use their course materials!

Important Dates

Course project proposal: 3/20 (due 11:55pm on Moodle)
Course project milestone: 4/5 (due 11:55pm on Moodle)
Final course project write-up: 5/1 (due 11:55pm on Moodle)


The Course Project is an opportunity for you to apply what you have learned in class to a problem of your interest.

Your are encouraged to select a topic and work on your own project. Potential projects usually fall into these two tracks:

Here you can find some sample project ideas professor provided last year:

To inspire ideas, you might look at recent deep learning publications from top-tier vision conferences, as well as other resources below.

For applications, this type of projects would involve careful data preparation, an appropriate loss function, details of training and cross-validation and good test set evaluations and model comparisons. Don't be afraid to think outside of the box. Some successful examples can be found below:

Deep neural networks also run in real time on mobile phones and Raspberry Pi's - feel free to go the embedded way. You may find this TensorFlow demo on Android helpful.

For models, deep neural networks have been successfully used in a variety of computer vision and NLP tasks. This type of projects would involve understanding the state-of-the-art vision or NLP models, and building new models or improving existing models. The list below presents some papers on recent advances of deep neural networks in the computer vision community.

We also provide a list of popular computer vision datasets:

Grading Policy

  Final Project: 40%
  milestone: 5%
  write-up: 10%
   •  clarity, structure, language, references: 3%
   •  background literature survey, good understanding of the problem: 3%
   •  good insights and discussions of methodology, analysis, results, etc.: 4%
  technical: 12%
   •  correctness: 4%
   •  depth: 4%
   •  innovation: 4%
  evaluation and results: 10%
   •  sound evaluation metric: 3%
   •  thoroughness in analysis and experimentation: 3%
   •  results and performance: 4%
  poster: 3% (+2% bonus for best few posters)

Project Proposal

The project proposal should be concise (200-400 words). Your proposal should contain:

Project Milestone

Your project milestone report should be between 2 - 3 pages using the provided template. The following is a suggested structure for your report:

Submission: Please upload a PDF file named <your ID>_milestone.pdf to Moodle. One submission for each group is sufficient.

Final Submission

Your final write-up should be between 6 - 8 pages using the provided template. After the class, we will post all the final reports online so that you can read about each others' work. If you do not want your writeup to be posted online, then please let us know at least a week in advance of the final writeup submission deadline.

Submit your final submission through Moodle. You will submit one or two files:
  1. A pdf file of your final report
  2. (OPTIONAL) zip file (or pdf file) with Supplementary Materials
Report. The following is a suggested structure for the report: Supplementary Material is not counted toward your 6-8 page limit.
Examples of things to put in your supplementary material: Examples of things to not put in your supplementary material:

Example Project Reports

COMPSCI 682 (2017 Fall) projects (only the project titles are available)
COMPSCI 697L (2016 Fall) projects
Stanford CS231n (2015 Winter) projects.

Computing Resources

Amazon offers free AWS credits for students (annually renewable). Using your link provided by the GitHub Student Developer Pack will get you the most free credits.

Collaboration Policy

You can work in teams of 1~2 people. We do expect that projects done with 2 people have more impressive writeup and results than personal projects.

Honor Code

You may consult any papers, books, online references, or publicly available implementations for ideas and code that you may want to incorporate into your strategy or algorithm, so long as you clearly cite your sources in your code and your writeup. However, under no circumstances may you look at another group’s code or incorporate their code into your project.

If you are doing a similar project for another class, you must make this clear and write down the exact portion of the project that is being counted for COMPSCI 682.