The purpose of group project is to creating opportunities and evaluating the skills necessary for students to independently acquire new, challenging knowledge, leveraging all available resources in their environment, and doing so in a timely fashion because promptness is of utmost importance.
Students are required to self-organize into 4-person groups to independently learn and execute a deep learning AI project promptly and resourcefully, with minimal supervision.
In this project, you have the freedom to choose a topic that truly ignites your passion, and select a project type that aligns with your interests.
The only criteria, it must be interesting, challenging, and ultimately useful to you.
Your passion will be your compass, guiding you on a journey of exploration and innovation.
Students typically undertake one of FIVE types of projects:
- 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.
It is very important for students to choose applications or research that they are interested in to explore deep learning of AI, which may bring many special ideas and new discoveries. Therefore, it is crucial to choose a project that ignites your passion and enthusiasm. Don’t hesitate to come up with ambitious ideas that excite you, and remember, if you need guidance on how to get started, we’re here to help.
Group Project Assessment:
- Project Team Formation (Week 3)
- Create a team of 5 people and choose a team leader.
- The team leader is responsible for submitting the group project assessment materials, such as member list, proposal, and final report.
- Send the list of team members and the elected team leader to the Instructor at eelmpo@cityu.edu.hk
- Deadline: Sep 20, 2024 2024
- Project Proposal (Week 4)
- A 5-page project proposal (not include references).
- Submit the project proposal in PDF format to CANVAS proposal assignment.
- Proposal must contain:
- Project Title
- Student Name, Student ID and Email Address of each member
- Summary with goals of the project in about 300 words.
- Other suggested content: The group proposal outlines the problem, objectives, methodology, dataset, baseline selection, experimental setup, timeline, evaluation plan, collaboration plan, risks, contingency plans, ethical considerations, and references for the group project. It serves as a roadmap, ensuring clarity and alignment among team members.
- References
- Deadline: Sep 27, 2024
- Oral Presentation (Week 12 and 13)
- The oral presentation assesses students' communication skills, including their ability to clearly convey project objectives, methodology, and findings. Their response to questions gauges their understanding of the project and role within the team.
- Every group is also required to make a 10-minute Power Point presentation of their group project to the entire class.
- The presentation must include:
- A short description of the project and its objectives
- An explanation of the implemented algorithm and relevant theory
- A demonstration of the working program – i.e., results obtained when running the program
- Final Report, PPT, Source Code and Demo Video (Week 14)
- The final project report should be 30-60 pages in length, including references. A final report template was provided here:
- Final_Report_Template
- The structure outlined in this template serves as a flexible guide rather than a rigid blueprint for the final report. While the chapters and sections presented here are commonly found in research theses or technical reports, the specific nature of the undertaken research may necessitate variations in structure. Additionally, the order of items within chapters can be adjusted accordingly. The template reflects the traditional technical report structure, which aims to demonstrate a coherent line of argument across six chapters: introduction, literature review, research design, results, discussion, and conclusions.
- Demo video is required to be a 3-4 minute summary of the project.
- Students are also required to submit the Python source code of any implementation and PPT of the oral presentation for assessment.
- The final report must include an Appendix A for “Individual Contributions of the Group Project”, in which students provide detailed information about each team member's contributions to the group project. This includes describing their responsibilities, the tasks they completed, and the outcomes they achieved. This appendix is important for assessing individual's performance in the group project, which is necessary for meeting the requirements of professional accreditation of the course.
- All the PPT, Final Report and Source Code are required to submit to CANVAS Group Project Final Report
- Deadline: Dec 6, 2024
Project Hints
- Your passion is your compass.
- A high-quality project for EE4016 would be one that has the potential to be published or nearly published. It is anticipated that some students will continue working on their projects even after completing the course, with the aim of submitting their work to conferences or journals. To gather inspiration, you can explore recent research papers in the field of deep learning, particularly from conferences like ICML and NeurIPS.
- Once you have identified a topic that interests you, it is beneficial to search for existing research on related subjects using academic search engines such as Google Scholar.
- Another crucial aspect of project design is identifying suitable datasets for your chosen topic. If the data requires significant preprocessing or you plan to collect it yourself, keep in mind that this is just one part of the overall project work and can often consume a substantial amount of time. Nonetheless, it is important to maintain a solid methodology and engage in thorough discussions of the results, so be mindful of pacing your project accordingly.
- For Application type group projects, here are some suggested AI-Powered Applications topics. Use these ideas as a starting point to learn how to build an AI application, then modify and enhance them to create your own unique solution.
Presentation Schedule:
Section A (Week 12)
Group 1 : Application of Deep Learning in Fungus Identification
- YUE Zijun (Leader), JIN Ziqi, ZHANG Ziang, ZHAO Zixun
Group 2 : Speech and Facial Detection for Psychology and Psychiatry
- Yiu Wing Chi (Leader), Chen Tsz Ching, Liu Pui Ying, Mok Cheuk Shan
Group 3 : Vehicle Type Recognition System
- Ng Chung Wah (Leader), Chung Tin Ho, Chan Chak Fung, Kuok Chun Cho
Group 4 : Custom Image Classifier for Pet Species Identification
- Lee Tao (Leader), Fung Chi Kit, Fong Yiu Fai, Chow Timmy
Group 6 : AIGC about Audio Enhancement by using Stable Audio
- Ling Ho Yin (Leader), Chan Russell, Cheung Hei Lok
Section B (Week 13)
Group 5 : Personalized Learning Assistant
- RAO Megha Badrinath (Leader), HARNE Aastha Mukund, CHANGANI Tanush Deepak, BHARGAVA Aryank, Jens Kjaersgaard Larrañaga
Group 7 : AI File Management Assistant
- Hong Chiu Wing Timothy (Leader), Lai Ching Hin, Angdriyanto Dennis
Group 8 : Automatic Depression Detection System Using Multimodal Data: Deep Neural Network Approach
- PENG Jingqi (Leader), ZHANG Yizhi, Cheung Wing Chun, Zhang Jiachang, Ding Jianlong
Group 9 : MSCOCO OBject Detection
- Fung Yat Chun (Leader), Ng Tik Fung, Ng Kin Fung
Group 10 : AI for Detecting AI-Generated Images
- Chu Ho Ming (Leader), Ko Ho Yin, Chow Yui Hong, Wu Long Hei, Ho Chi Long
Group 11 : Deep Reinforcement Learning Applications in Probability-Driven Scenarios (Big Two Card Game)
- Lee Ling Ling (Leader), Fu Hon Nam, Ngai Tung Lam, Mok Ching Wang
Group 12 : Smart Agriculture: Leveraging Deep Learning for Accurate Crop Pest and Disease Detection
- Muhammad Sahrish ISLAM (Leader), Mahendra Raffi Surya PERMATA, Vanessa Laurel HARIYANTO, Ezra Rephael KARJANTORO
Group 13 : Music Type Classification for Recommendation Systems
- Kong Ko Lun, Sean (Leader), Leung Chung Ming, Chan Chin To
Group 14 : In-A-Row
- LIANG Chengchang (Leader), HUANG Tak Chun
Group 15 : Deep Learning for Underwater Garbage Recognition: Enhancing Environmental Conservation
- Poan Tsz Kiu (Leader), Cho Wei Ting, Choy Sin Ying, Man Heung Wo
