What to Do
When to Do It
Neural networks are one of the most broadly applicable machine learning tools we’ve seen in the course, and can be used in a wide variety of different settings. Instead of completing Traffic, you have the option for this unit to complete an exploratory project, where you build a neural network for a classification task of your choosing.
To complete the exploratory project, you should:
- Pick a topic area of interest to you: options include art, biology, business, energy, engineering, epidemiology, finance, government, language, law, medicine, music, sports, and more.
- Find a data set about the topic.
- Send a brief proposal email to Brian and your assigned teaching fellow describing what topic you’d like to explore and what data set you plan to use.
- Once your project is approved, build a neural network using TensorFlow to perform a classification task based on your data set.
- Submit the code for your network along with a
README.mdfile that describes, in one or more paragraphs, how to run your program.
Data sets of interest might include
- Census data
- Global COVID-19 data
- U.S. government open data
- Kaggle data sets
- any other data set of your own interest and choosing!
This project will be graded on the basis of correctness, design, and style.
- Correctness refers to the extent to which your code is consistent with the project’s specifications and free of bugs.
- Design refers to the extent to which your code is written well (i.e., efficiently, elegantly, and logically).
- Style refers to the extent to which your code is readable (i.e., clear, consistent, commented, indented, with variables aptly named). This includes, but is not limited to, adherence to Python’s style guide, which you can verify using