Overview

The final project is your opportunity to synthesize everything you’ve learned in the course into a meaningful and original application. Your goal is to tackle a real-world or scientific problem, combining domain knowledge with computational modeling and machine learning in a rigorous, creative, and insightful way.

This project is not just about performance metrics; it’s about:

  • Framing a well-posed scientific question
  • Designing and analyzing machine learning models in context
  • Iterating thoughtfully by analyzing results, going back to the data, and refining your model
  • Demonstrating a deep understanding of your problem with a clear plan for future work

Final Presentation

The final presentation of your work will be on Wednesday, April 30 at 10:00am. The format is 5-minute presentation + 5-minute Q&A; you will get points for asking questions as well. The presentation should be roughly structured as:

  • Motivation, question, and background
  • Methodology and approach
  • Key results and analysis
  • Reflections and future directions Plan your time accordingly. 5 minutes is not a lot of time, so make sure to practice your presentation.

Final Report

  • Due: Sunday, May 4 (at 23:59)
  • Length: 6 pages (max), in a conference-style format (e.g., IEEE, NeurIPS, ICML)
  • Contents: (order and structure is flexible. Choose what is most appropriate for your project.)
    • Abstract
    • Introduction and Motivation
    • Related Work (with citations)
    • Problem Formulation
    • Methodology
    • Results & Analysis
    • Conclusion & Future Work
    • References
    • Link to GitHub Repository

The GitHub repo must contain: all relevant code, scripts, and notebooks. It should also have a README file with: setup instructions, description of the project structure, and clear instructions for reproducing results. Find tutorials online for how to set up a GitHub repo if you need to.

Evaluation Criteria

Your final project will be evaluated based on the following criteria:

Criteria Description
Creativity, Novelty, and Relevance Is the project original, insightful, or tackling a meaningful problem?
Report Quality Is the report well-written, clearly structured, and within the 6-page limit?
Literature Awareness Have you reviewed and referenced relevant prior work appropriately?
Technical Rigor Is there a solid mathematical formulation, and is it suited to the dataset/context? Express yourself mathematically whenever possible.
Use of Domain Knowledge Have you incorporated prior knowledge into the model design and hypothesis space?
Scientific Thinking Did you iterate, test hypotheses, and refine your model based on results?
Model Justification Are the chosen methods justified in relation to the problem and data?
Results & Analysis Are the results critically analyzed and compared across models or approaches?
Future Directions Are thoughtful improvements or extensions proposed?
Code Quality & Reproducibility Is the code clean, documented, reproducible, and accessible via GitHub?
Delivery & Follow-through Did you fulfill the goals promised in your original proposal?
Timeliness Late submissions will be penalized

Final Remarks

  • Start early and ask questions early.
  • Your depth of understanding matters more than flashy results.
  • Ask questions. Meet with me or share roadblocks early if you get stuck.

Let this project be a chance for you to explore, create, and think like a scientist.