Group Project Overview
Today's advanced Large Language Model (LLM) technologies, such as OpenAI o3, Google Gemini-2.5 Pro, and DeepSeek-R1, have revolutionized the way we learn, and are best suited for "learning by doing" — a hands-on experience that prioritizes practical experiments. The method is superior to the traditional theory-first approach. At the heart of this approach is the importance of working on a meaningful project—one that sparks passion, excites curiosity, and motivates us to dive in and see it through to completion.
Project Structure
Students must form teams of five members by Week 3 and select a team leader. The team will independently take on a deep learning AI project that both excites and challenges them, with minimal supervision. The team leader will be responsible for submitting all group materials, including the member list, project proposal, and final report. This structure encourages collaboration, initiative, and ownership of the learning process.
Project Types
⭐ Create Your Own Data and use an existing approach
The main focus is on collecting data and making it useful for existing DL methods. A good example is that you collect some images and annotate them, and then train an existing image classifier to perform a specific task on these images.
⭐⭐ Create Your Own Method on an existing dataset
Taking an existing dataset and adapting an existing method to make it your own DL method. You modify parameters, work with existing neural networks, apply what you've learned in the lecture, and aim to make them more efficient.
⭐⭐⭐ Beat the Classics - Implement a DL method and compare to non-DL baseline
Challenge the state-of-the-art using algorithms without DL or with DL algorithms that aim to surpass this baseline. Compare the results and demonstrate the ability to outperform the baseline, referred to as "beat".
⭐⭐⭐ Create Your Own AI-Powered Application
Create a practical AI-powered tool or service addressing a real-world problem using advanced AI and deep learning technologies. Demonstrate creativity and technical proficiency in a functional solution.
⭐⭐⭐⭐ Beat the Stars - Improve the state of the art
By selecting a research paper, the goal is to demonstrate how to outperform the current state-of-the-art papers. It's important to note that due to the rapid pace of paper publication, it might be difficult to stay up-to-date with the latest advancements. Nonetheless, the objective is to select a recent paper as a baseline and attempt to beat it.
Group Project Assessment Timeline
Project Team Formation (Week 3)
- Create a team of 5 people and choose a team leader
- Team leader is responsible for submitting all group project materials
- Send team member list and elected leader to: eelmpo@cityu.edu.hk
Project Proposal (Week 4)
A 5-page project proposal (not including references) submitted in PDF format to CANVAS.
Required Content:- Project Title
- Student Name, Student ID and Email Address of each member
- Summary with goals (300 words)
- Problem statement and objectives
- Methodology and dataset description
- Baseline selection and experimental setup
- Timeline and collaboration plan
- References
Oral Presentation (Week 12 and 13)
10-minute PowerPoint presentation to the entire class assessing communication skills and technical understanding.
Presentation must include:- Short description of project and objectives
- Explanation of implemented algorithm and relevant theory
- Demonstration of working program with results
Final Technical Report and Deliverables (Week 14)
Minimum 25-page final report following technical report structure.
Required Submissions:- Final technical report (PDF) - 25+ pages minimum
- PowerPoint presentation file
- Python source code
- 3-4 minute demo video summary
- Appendix A: Individual Contributions Report
Template Technical Report: Use this technical report template with LaTex. Must include Appendix A for individual contributions assessment.
Project Hints and Resources
Key Principles
- Your passion is your compass - Choose topics that excite you
- Quality standard - Aim for publishable quality work
- AI tools - Use Elicit, SciSpace, Consensus, NotebookLM for research
- Academic resources - Use Google Scholar for literature review
- Dataset considerations - Plan data collection/preprocessing time carefully
Presentation Schedule
Section A - Week 12 (Nov 20)
Section B - Week 13 (Nov 27)
Department of Electrical Engineering, City University of Hong Kong
