An example proposal has been attached; this is for a graduate-level python programming class.
Objectives
- Research Articulation: Cultivate the ability to clearly define, describe, and contextualize a machine learning research project, ensuring its relevance and significance are evident.
Literary Integration: Develop proficiency in identifying, reviewing, and integrating existing research to provide a robust backdrop for the proposed project, showcasing an awareness of the current state of the art.
Methodical Planning: Acquire the skill of detailing the machine learning methodologies, data sources, and processing techniques pertinent to the project, ensuring replicability and transparency.
Evaluation Acumen: Enhance the ability to select and justify relevant evaluation metrics, aligning them with the nature and goals of the research.
Ethical Awareness: Foster a keen understanding of the moral dimensions of machine learning research, promoting responsible and conscientious research practices.
Anticipative Strategy: Cultivate the ability to foresee potential challenges, limitations, and obstacles and pre-plan strategies or mitigative measures.
Directions
- Write a Proposal for your project that you will complete for the second half of this course.
- What to Include
- This proposal should be (1500 to 2000 words) and include:
- Title: The title should be clear, concise, and descriptive of the research project.
- Introduction: This should briefly explain what machine learning is and the relevance of their specific project. It should also explain why the project is essential and the problem(s) it will address. This section should end with a clearly stated research aim or question or hypothesis.
- Literature Review: The students should identify and briefly review existing research on their proposed project. This will help to contextualize the project and show that they understand the state of the art in their area of interest.
- Methodology: This section should describe the machine learning method(s) they plan to use for the project, including why they are suitable. It should also explain the data they will use (e.g., what it is, where it will come from, and how it will be collected and processed). The methodology should be detailed enough that someone else could reproduce the study.
- Evaluation Metrics: How will the success of the project be measured? This could include things like accuracy, precision, recall, F1 score, Area Under the Curve (AUC), Mean Squared Error (MSE), and others, depending on the nature of the project.
- Timeline: A proposed timeline for the project’s different parts helps keep the work on track. It also allows you, as the instructor, to assess the project’s feasibility within the time constraints of your course.
- Ethical considerations: Considering increasing awareness of the ethical implications of AI and machine learning, it discusses potential ethical issues related to their project. This could include topics related to data privacy, fairness and bias, transparency and explainability, and others.
- Potential Limitations: This section should discuss potential obstacles, limitations, or challenges encountered during the project and how these can be addressed.
- References: All sources of information used in the proposal should be correctly cited, following the citation style you stipulate for the course.
- (OPTIONAL) Appendix: If there are any additional diagrams, code snippets, or related materials, they can be included here.
