Class Project Information
Teams
The final project will be done in teams of 2-3 people. Please fill in the team assignment form before Nov 23.
Important Milestones
| Deadline | Task |
| Nov 23 | Submit group assigment |
| Nov 30 | Submit project proposal |
| Dec 8 | Complete meeting with course staff |
| Jan 4 | Submit poster PDF file (Submission will be closed at 11:59am) |
| Jan 6 | Poster session |
| End of the semester | Submit final report |
Proposal: The proposal should be no more than one page (single-space, excluding references). It should briefly describe the topic, background, related work, problem and proposed method of your project.
Meeting with course staff: After submitting the proposal, please arrange a meeting with the instructor or one of the TAs get feedback on the project proposal.
Poster Presentation: We will hold a poster session to showcase your project on the final week. Each group need to submit a poster in PDF format of A0-size to Web Learning before Jan 4th. You will present your work in our final class on Jan 6th. Poster session will be held online.
Final Report: Each group should submit a written report in a single PDF document with at most 4 pages. All related materials, such as source code, should also be provided.
Poster template (new!)
Project Topic
The project topic can be very open-ended. You may choose any topic you are interested in (such as your research area) as long as it pertains to the course material. You are encouraged to explore advanced machine learning techniques in supervised, unsupervised and reinforcement learning.
Usually, your project will fall into one of the following categories:
Application: apply a machine learning method to solve a specific problem, e.g.
Machine learning for AI applications in computer vision, NLP, speech recognition, etc
Use a learning-based approach for physical systems, such as energy, material and optics
Algorithm: develop a machine learning method that has better performance, e.g.
Improve the robustness of an algorithm under noisy data
Make a machine learning method more efficient
Theory: theoretical problems, e.g.
Theoretical analysis of a specific learning algorithm regarding its generalization, consistency, or stability properties
In-depth tutorial of a particular theoretical topic
A good project will showcase your ability to clearly define a problem, understand the motivation and technical details of your method, and complete significant implementation with real data.
Grading
The final project will count for 25% of your final grade. It will be graded on a scale of 100 points, which include:
Proposal: 15 points
Discussion with class staff: 5 points
Final poster: 25 points
Poster session participation: 5 points
Final report grade: 50 points
Sample Posters, Projects from Previous Years
Poster
Chinese Rap Generation, Chen Zhu and Xuzhe Wangchin (application)
AI Coach for divers, Lekang Yuan and Xiaohang Yu (application)
Face Rectification on Feature Maps for Recognition, Peng Lu (algorithm)
Report
Deep Hypergraph Convolutional Network for Paper Classification, Yifei Zhu and Guanzi Chen (algorithm & theory)
Machine Learning in Casual Inference: A Survey and Application in Economics, Pengxiang Zhou and Yanyu Chen (application)
An Application of Machine Learning on Predicting CO2 Reduction Electrocatalysts, Chen Liang and Yuhang Zhang (application)
Live Gradient Compensation for Evading Stragglers in Distributed Learning Jian Xu (algorithm)
Debugging Neural Networks, Riccardo Mattesini, Sebastian Beetschen and Bunchalit Eua-arporn (algorithm)
Common Pitfalls
Simply run a model from an existing work on a slightly different dataset: think of your contribution in at least one of the three catogries discussed in the Topics section
Topic is too broad or too ambitious: reduce the project scope
Project relies heavily on data collection: use available datasets