Course Project
The course project is your chance to apply ML to a problem that genuinely interests you. Projects can follow one of two broad tracks:
- Applications: apply ML methods to a real problem in your domain of interest.
- Methods: develop or extend algorithms and evaluate them rigorously.
Your project must involve data and empirical evaluation. Ambitious, well-scoped projects that demonstrate thoughtful methodology and clear analysis are encouraged.
Important Dates
Unless otherwise noted, all items are due at 11:59pm local time.
| Deliverable | Weight | Due Date | Late Days |
|---|---|---|---|
| Project Proposal | 5% | 2025-10-02 | Yes |
| Project Milestone | 5% | 2025-10-30 | Yes |
| Final Report | 15% | 2025-12-10 | No |
| Final Presentation/Poster | 5% | (TBD) | No |
Overview
Choose a problem/topic that excites you. Example directions include:
- Predictive modeling on a curated dataset (classification, regression, forecasting).
- Representation learning and feature engineering for a specific task.
- Comparative study of algorithms for a well-defined problem.
- Re-implementation and careful evaluation of a recent paper.
Projects may be individual or in teams of up to 3. Larger teams are expected to deliver correspondingly deeper scope, stronger analysis, and clearer takeaways.
Collaboration and Honor Code
You may consult books, papers, public repos, and online resources, provided you cite them clearly in your report and code. Do not copy others’ code verbatim without attribution. If this project overlaps with another course or research effort, clearly delineate the portion that is unique to this course.
Late Policy
Late days apply to the proposal and milestone only. The final report and presentation have fixed deadlines.
Project Proposal
Submit a brief proposal (200–400 words) that addresses:
- Problem statement and motivation (why it matters)
- Related work you will draw on
- Data you will use (source, size, access, preprocessing needs)
- Methods you plan to try (baseline first, then improvements)
- How you will evaluate results (metrics, baselines, ablations)
Submission: one PDF per team via the course submission system. Include team members and emails.
Project Milestone
2–3 pages using the provided template. Include:
- Title and team
- Refined problem statement and dataset details
- Technical approach so far (with diagrams if helpful)
- Preliminary results/figures and what you learned
- Updated plan and risks
Submission: one PDF per team.
Final Report
6–8 pages using the course template, written like a short paper. Suggested structure and grading rubric:
- Title, authors
- Abstract (≤300 words)
- Introduction (motivation, problem, contributions)
- Related Work
- Data (source, statistics, preprocessing)
- Methods (clear, reproducible description; include ablations where relevant)
- Experiments (metrics, baselines, comparisons, figures/tables)
- Conclusion (key takeaways, limitations, future work)
- References
Also submit minimal supplementary material as needed (e.g., small demo video or link to code repository).
Presentation
Short in-class or poster-style presentation during finals week. Aim to communicate the problem, approach, and main insights clearly to a general ML audience.
Resources and Inspiration
- Top venues for recent ideas: NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV.
- Public datasets: UCI, Kaggle, OpenML, Hugging Face Datasets, COCO/Cityscapes (vision), LAION (vision-language), and domain-specific repositories listed on the course Resources page.
If you need feedback on scope or dataset choice, reach out early.