Project
The final project is designed to give you experience implementing a machine learning model on a real-world dataset—without me telling you what method to use. We want you to explore any creative or unconventional methods you can think of! This is a completely open-ended problem, and the most important thing I want to see is effort. Details for 2026 have been released! So let’s go over the project details:
Topics
There are three project topics covering a range of machine learning tasks:
- Identify birds in nature (computer vision) [Kaggle] — You will be given images containing birds in nature. Your task is to identify and draw bounding boxes around the birds.
- IMDB positivity (sentiment analysis) [Kaggle] — You are given a collection of IMDB movie reviews and your job is to classify the reviews as possitive or negative.
- LLM or not( prediction) [Kaggle] — You are given a collection of essays and need to figure out which essays were written by students and which were generated by LLMs.
Dataset
On the Kaggle competition page.
Kaggle Competition
To make things more exciting, we are hosting a competition on Kaggle! You will submit your best runs to Kaggle (details are in the project description files).
The top individual or team for each topic will receive a prize:
- Individuals: Full letter grade bump
- Teams (two or more members): Half-letter grade bump
We hope this will encourage you to try new methods and explore the current state-of-the-art models in machine learning.
We will be verifying the winners and ensuring the submitted numbers come from models that follow the competition rules.
Important: Make sure to set a random seed so that your training is reproducible.
Teams
You may work in teams of up to three people and only need to complete one of the three project topics. There’s a variety to choose from, so pick the one that interests you most!
Deliverables
You must submit:
- Your code
- A two-page report describing what you did, what you tried, whether it worked, and what you would do differently next time.
Due Dates
There are two important deadlines:
- Kaggle competition closes: May 6th (Wednesday before Reading Day)
- Two-page report due: May 11th (Monday)
Winners!
To be announced