Project Pitches
Please pitch a project idea in the #project-pitches channel on EdStem. Your project idea will not necessarily be the final project you work on — indeed, many of you will work on someone else’s idea. That’s ok! I am still expecting everyone to make a pitch.
First, read the course description of the project. Then, think about what you’d like to do!
Second, write your pitch! To write your pitch:
- Write one or two sentences about the big picture: what problem you’d like to address and how machine learning fits in.
- If your project needs data (almost all will), state your data plan. There are three ways:
- Describe a data set to which you currently have access.
- Link to an online data set that is suitable for addressing your problem.
- Describe a specific approach by which you will collect your own data.
- State what kind of problem your pitch involves. Is it a classification problem, a regression problem (predicting a quantitative outcome rather than a qualitative label), or an unsupervised problem like clustering?
- Describe how you’ll judge whether your project is successful. What are you looking to have produced/achieved by the end of the semester?
- Close your pitch by letting us know: why are you excited about this topic?
Where Can I Look for Data?
I expect that most projects will involve the use of data in some way for analysis or experimentation. There are several very common sources of data sets across a wide range of domains, including:
- Kaggle
- UCI Machine Learning Repository
- Tidy Tuesday, a social project by the R community that releases a new data set for analysis very week.
There are also many data sets for machine learning available through other channels, including many that are published as companions to scholarly papers. If you need help finding a data set that matches your interest, please let me know and we can talk about it!
© Phil Chodrow, 2025