This page describes the fourth of five project milestones.
Instructions
The presentation draft is a vital stage in the overall assignment. It provides an opportunity for teams to shape their findings, critical analyses, and thoughts on the selected paper into a form that can be presented to a technically knowledgeable audience. The objective is to offer a clear, engaging, and thorough exploration of the paper that not only elucidates the work’s substance but also guides the audience in understanding its relevance, assessing its quality, and applying its methods or findings. The following sections should be included:
Introduction
- Title slide: The title slide should include your names, the class and project name, and the date the presentation is given. In addition to setting the context for your audience, these details are useful when presentation slides or videos are referenced by colleagues later.
- Topic background: Provide an introduction to the area of deep learning or digital signal processing that the paper addresses. What problem does the paper seek to solve?
- Paper selection justification: Briefly explain why this paper was chosen, considering its relevance, methodology, and impact on the field.
- Objectives of the presentation: State what you intend the audience to learn from your presentation.
Paper Overview
- Authors and publication details: Briefly introduce the authors and mention where and when the paper was published.
- Abstract summary: Summarize the paper’s abstract to provide an initial understanding.
Methodology
- Deep neural network architecture (if applicable): Explain the architecture used in the paper. Provide insights into why this architecture was chosen.
- Data collection/augmentation/error metrics (if applicable): Detail any specific methodologies, tools, or technologies highlighted in the paper.
Key Contributions and Results
- Main findings: Highlight the key contributions and results of the paper.
- Visualizations: Include diagrams, charts, or graphs that visually represent the paper’s findings.
Critical Analysis
- Strengths and weaknesses: Discuss the strengths and weaknesses of the paper, as identified in your critical notes.
- Reproducibility: Comment on whether the work seems sound and if there’s enough information provided to reproduce the methods.
Relevance and Application
- Impact on the field: How does this paper contribute to the broader field of study? How could it be applied in real-world scenarios?
- Recommendations: Based on your analysis, offer suggestions or insights into further exploration or application.
Technical Demonstration (Optional)
- Code examples: If applicable, showcase any code snippets or live demonstrations that help in understanding the methodology or results.
Conclusion
- Summary: Recap the main points of your presentation.
- Call to action: Encourage the audience to explore further or consider how they might apply this knowledge in their work.
Questions and Discussion
- Prepared answers: Be ready with answers to possible questions and encourage discussion during or after the presentation.
References
- Citation of papers: Include proper IEEE citations for the papers being presented and any other sources referenced.
By following this structure, student teams will be able to create a presentation that not only details the academic content of their chosen papers but also engages their colleagues in a manner that encourages further exploration and application of the topics at hand. Ensure that the content is accessible to those with a similar technical background and make effective use of visual aids to enhance understanding.
Requirements
- Collaborate with your teammates to address all aspects of the assignment instructions.
- One member of your team should submit your deliverable—slides, code examples, or other materials outlining the content of your presentation—in Canvas by the posted due date.
- Review the presentation draft rubric posted on Canvas for grading criteria.