Why choose this project topic?
A study of choosing which unlabelled examples should receive limited human annotation gives this topic a concrete purpose beyond simply adding an AI model. Comparing performance per label, selection bias and run variability helps a student demonstrate capability, inspect failure cases and explain where human review or a simpler method remains necessary.
How effectively can active learning annotation budget support choosing which unlabelled examples should receive limited human annotation, evaluated using performance per label, selection bias and run variability?
Choose a feasible licensed or synthetic dataset and available compute budget, then agree a narrow evaluation for choosing which unlabelled examples should receive limited human annotation with your supervisor.
Proposed project objectives
- 01Define the task, evidence and evaluation assumptions for choosing which unlabelled examples should receive limited human annotation.
- 02Use a licensed labelled dataset with labels temporarily hidden, compare uncertainty sampling with random selection and repeat across seeds.
- 03Measure performance per label, selection bias and run variability against a stated baseline and analyse failure cases.
A suggested research approach
Use a licensed labelled dataset with labels temporarily hidden, compare uncertainty sampling with random selection and repeat across seeds. Check dataset permissions and keep evaluation examples separate from model development. Record model versions, prompts or training settings, then compare performance per label, selection bias and run variability using repeatable runs and retain incorrect outputs for a transparent limitations discussion.
What you will need
- Licensed labelled dataset
- Active learning tools
- Fixed annotation budget
Keep your project scope clear
Simulated annotation cost may differ from real human effort or disagreement. Generated project writing does not include a trained or deployed AI application.
Artificial Intelligence project chapter outline
Use this outline as a starting point. You can edit the chapter titles to match your department’s format during setup.
- Chapter 1Introduction
- Chapter 2Literature Review
- Chapter 3System Analysis and Design
- Chapter 4System Implementation and Testing
- Chapter 5Summary, Conclusion and Recommendations
Turn this topic into your own final year project.
Your title, department, research question and outline are ready. Add your institution, personalise the details and continue to your project workspace.
Generate the Complete Project Generation uses your word balance. Review the draft and supply your own verified research findings.