Deep Learning Day Fall 2025

About the challenge

You will complete final projects in groups of 3-4 people. These projects are an opportunity for you to apply the knowledge you've gained in class to a topic or area of interest. Your group may implement research papers, existing projects, or create entirely new projects. **The time commitment per student should be *approximately equal regardless of the group size*, thus larger groups should justify their group size by taking on more ambitious projects.**

Your project group, once assembled, will be assigned a **mentor TA**, who will guide you throughout the remainder of this semester. Your mentor will be the one to give you pointers on how to get started, where to look for ideas, and also be one of the people evaluating your work. You will be graded on completion of goals, punctuality with which you meet your deadlines, and professionalism in reports and presentations.

We do not expect you to build a magical model that solves all of the world's problems. What we do expect is concentrated and well thought out effort into solving a problem: Rather than reporting a single number using a certain metric and showing your model just "works", we expect you to perform **quantitative ablation studies** (e.g. show us what architectural changes, hyper parameters, and regularization techniques you tried and what are helpful, explain why you think so) to analyze your models, along with **qualitative evaluation** (e.g. visualizations) to illustrate what the model learns and how it might fail. You might want to check Prof. Bill Freeman's awesome talk on "How to Write a Good CVPR Paper" ([video](https://www.youtube.com/watch?v=W1zPtTt43LI&t=2681s), [slides](https://billf.mit.edu/sites/default/files/documents/cvprPapers.pdf)). Of course, we do not expect you to finish a project at "top AI conference" level within a semester, but the general high-level principles are helpful.

Do not be afraid of negative results! Some of the most interesting and well-received presentations from past years were ones that failed to produce "successful" results!