Final Project Information for 2025 Fall UVa CS -ML-Undergraduate

Each week, Prof.Qi share one idea on potential projects or project domain: for example,

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Week2 Idea:

• Working with AI: Measuring the Occupational Implications of Generative AI
• Summary: Given the rapid adoption of generative AI and its potential to impact a wide range of tasks, understanding the effects of AI on the economy is one of society’s most important questions. In this work, we take a step toward that goal by analyzing the work activities people do with AI, how successfully and broadly those activities are done, and combine that with data on what occupations do those activities. We analyze a dataset of 200k anonymized and privacy-scrubbed conversations between users and Microsoft Bing Copilot, a publicly available generative AI system. We find the most common work activities people seek AI assistance for involve gathering information and writing, while the most common activities that AI itself is performing are providing information and assistance, writing, teaching, and advising. Combining these activity classifications with measurements of task success and scope of impact, we compute an AI applicability score for each occupation. We find the highest AI applicability scores for knowledge work occupation groups such as computer and mathematical, and office and administrative support, as well as occupations such as sales whose work activities involve providing and communicating information. Additionally, we characterize the types of work activities performed most successfully, how wage and education correlate with AI applicability, and how real-world usage compares to predictions of occupational AI impact.

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Week4 Project ideas:

• Machine Learning at UVA (ML@UVA), the university’s leading machine learning club with 300+ members and faculty advisors. We collaborate with UVA departments, research labs, and industry partners to tackle real-world challenges through data-driven innovation.

• This year, they are collaborating on joint projects with Logistics Management Institute (LMI) and Johns Hopkins APL. Since your course covers machine learning fundamentals and AI techniques, and we are pursuing projects in neural networks, computer vision, graph analysis, and anomaly detection

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• Week6 Example: Working with AI: Measuring the Occupational Implications of Generative AI
• Week7 Example: Working with AI: Measuring the Occupational Implications of Generative AI
• Week8 Example: Working with AI: Measuring the Occupational Implications of Generative AI
• Week9 Example: Working with AI: Measuring the Occupational Implications of Generative AI
• Week10 Example: Working with AI: Measuring the Occupational Implications of Generative AI
• Week11 Example: Working with AI: Measuring the Occupational Implications of Generative AI
• Week12 Example: Working with AI: Measuring the Occupational Implications of Generative AI
• Week13 Example: Working with AI: Measuring the Occupational Implications of Generative AI

• Week14 Example: Working with AI: Measuring the Occupational Implications of Generative AI

• Many many more!

Looking forward to your demos and artifacts!

Detail requirements on Final Project and required artifacts:

• On what to create / submit for your final project:
  • A mini demo presentation to the instructors on your project idea is expected. The mini presentation is expected to explain (WHY/ WHAT/ HOW on your project!)
  • A slide deck (Due in Canvas on xx ) summarizing your project and describing the results you reproduce; Filling in the template slide pages will be enough enough (more is better!).
  • Please formulate your project presentation using a given template
  • A python Jupyter notebook (Due in Canvas on Xxx, together with the slide deck) to present the code, data visualization, and obtain the results and analysis through step by step code cell run.
  • You are expected to go through and show the notebook file and your final project presentation to the instructors.
• On the final presentation of your project:
  • To minimize the overhead time cost (switching, wrong setup, ….), we will expect you to record a demo video to present your project, presentation contents including
    1. your project slide deck
    2. your project iPython Notebook and you cell run it in the demo video
    3. Please practice the whole process for a few times before you make video demos.
    4. we recommend you to submit your video demo to youtube and share the link in the Canvas project submission.
• On how we grade the projects:
  • Here is the grading rubrics we will use to grade your final report
  • Clear definition of problem and importance (25%)
  • Clear explanation of approach and code runs (25%)
  • clear summary of results (and/or difficulties) (25%)
  • Well-organized, polished as a whole (25%)