Final Report

Final Report (15%) #

The final report should present and compare at least 3 models that you have developed throughout the semester. Results should be presented using visualizations and quantitative metrics with a heavy focus on comparing the performance and tradeoffs of each approach. You will add information to your midterm to create your final report.

Note: As part of research, it is natural that the project may change from the original proposed. Please be sure to document and justify any such changes in this report.

Final Sections & Checklist #

  1. Introduction/Background: refer to guidance from the proposal checklist

  2. Problem Definition: refer to guidance from the proposal checklist

  3. Methods: Present multiple solutions including specific data processing methods and machine learning algorithms. Explain why your chosen models and methods were selected.

    ✅1+ Data Preprocessing Methods
✅3+ Algorithms/Models
✅CS 7641: Unsupervised and Supervised Learning Method Implemented
✅CS 4641: Supervised or Unsupervised Learning Method Implemented

  4. Results and Discussion: Discuss the results of your methods. Present visualizations and quantitative scoring metrics to analyze the performance of your models. Compare each approach and explain the tradeoffs, strengths, and limitations of each approach. What does your visualization/metric tell you? Why did each model perform well/poorly? How do the models compare to each other? What are the next steps if any?

    ✅Visualizations
✅Quantitative Metrics
✅Analysis of 3+ Algorithms/Models
✅Comparison of 3+ Algorithms/Models
✅Next Steps

  5. References: refer to guidance from the proposal checklist

Final Submission Requirements #

1. Report: The final report must be written on a website hosted with GitHub Pages or Streamlit. There is no word limitation for this deliverable. Reuse your midterm report and make the necessary updates and/or additions.

In addition to the 5 sections above, please include the following in your report:

  • ✅Gantt Chart: list each members’ planned responsibilities for the entirety of the project. Feel free to use the Fall and Spring semester sample Gantt Chart.
  • ✅Contribution Table: list all group members’ names and explicit contributions in preparing the final using the format below.
    Name Final Contributions
    Member1 Contributions
    Member2 Contributions
    … …

2. Hybrid Presentation: The final presentation is a 9-minute in-person or virtual session, followed by a 3 minute Q&A, that summarizes your project. You will coordinate scheduling with your project mentor, who will oversee presentations from all teams under their guidance. You are encouraged to use Microsoft PowerPoint, Google Slides, or similar tools. Clean, high-quality visualizations play a significant role in the mentor’s grading. You may also create an interactive visualization or web application to enhance your presentation. All group members are required to participate.

Some key criteria for evaluation include:

  • Introduction
    • Clearly explains the problem statement, data, and its significance in the ML context
  • Technical Content & Understanding
    • Presents model architecture and hyperparameters with clear justification and why those models are chosen.
    • Accurately discusses evaluation metrics and their appropriateness for the problem
  • Results & Analysis
    • Demonstrates proper interpretation of evaluation metrics
    • Provides meaningful comparisons between different approaches or model iterations
  • Presentation & Communication
    • Be clear and concise
    • Manage presentation time effectively if multiple team members are presenting (multiple team presentation is encouraged)
    • Use professional and effective visualizations (interactive demos are encouraged)
    • Respond thoughtfully to questions, showcasing deep project knowledge
    • Engage with other teams’ projects, offering thoughtful questions or feedback

3. GitHub Repository: Reuse the GitHub repository from the midterm, and add all relevant directories, files, and code. Update the README.md file explaining all relevant directories and files using the format below.

/dir/: Description of the directory
/dir/file.txt: Description of the file