The final project offers an opportunity for you to explore a topic in greater depth than we covered in class and to apply your machine learning knowledge to an interesting domain. For the project, you will identify a dataset and a task involving supervised classification, regression, or recommendations. The task by itself could be novel, or you could explore new ways of attacking it compared to existing work. The project is comprised of trying different feature representations and machine learning models, and evaluating their performance. Your completed project and final submission will be a paper (minimum 1,300 words) summarizing your dataset, methods, and experimental results, as well as your supporting data and code, which you will present to the instructor during reading period or final exam period.
There are three milestones for the final project, as described below, each with its own deadline. You are required to work in teams of 2 or 3 for the final project. Working in teams is the norm for computer science. If, for some reason, you prefer to work alone, you will need to get permission from the instructor.
Project outcomes will be evaluated on the amount of demonstrated effort, depth of research into the topic and into related work, creative problem solving, and demonstrated understanding of machine learning. You should aim to design correctly working programs that implement sensible algorithms and featurizers, and show that you made an effort to try multiple ideas.
There are three milestones for the project:
| Milestone | Percent of Final Project Grade | Due Date |
|---|---|---|
| Proposal | 10% | See course schedule |
| In-Class Presentation | 5% | See course schedule |
| Completed Project / Final Paper | 85% | At meeting with instructor scheduled during Reading Period or Final Exam Period |
For your final project, you are free to use data from any source. Below are a few possible sources (disclaimer - these sources are neither vetted nor endorsed by the course instructor), though you are by no means limited to these.
The project proposal sets out your topic and goals in a paper (minimum 350 words). The most important criterion for choosing a topic is that it genuinely excites you. Be creative! The second is feasibility -- you have limited time to work on your final projet, so set your goals realistically. Keep in mind that it will take significantly less time to use an existing well-formatted dataset than to collect the data yourself or use data that requires considerable pre-processing.
Once you have established your team, you should identify your dataset and think carefully about the following questions: Is there previous work from others using this data? If so, what have they found? What do the data represent? What format is the data in -- is it easily input into machine learning algorithms? Can the data be visualized? How will you featurize the data? What questions are you trying to answer? What machine learning algorithm or set of algorithms will you use? Are there hyperparameters and, if so, what are they and how will you tune them? How will you evaluate the accuracy of the algorithms you are employing? How else might you assess your algorithms beyond their accuracy? What strategies will you use to check for underfitting/overfitting and/or to improve the performance of your algorithm? How will the project challenge you beyond what you learned in the class?
In general, the more time you spend initially thinking deeply about the above questions, the more smoothly your project will go and the less time you will spend later on heading down dead ends. Your proposal, at least 350 words in length, should include the following:
During class, each team will share their project ideas and plan with the rest of the class. In advance, you should prepare a Google slides presentation that you submit to Gradescope. Your in-class presentation should be approximately 10 minutes in duration and should clearly convey to your classmates what you will be doing in your project. The presentation should include:
For your final submission, you should have complete, working code that processes and featurizes your data, and applies one or more machine learning algorithms toward analysis of the data. You will submit a paper (minimum 1,300 words) that summarizes relevant literature, your methods, experimental results, and ideas for future work. Your final paper should include the following:
You must present your completed project and final paper to your instructor during reading period or final exam period. This is not a formal presentation but rather a meeting with the instructor where you describe your final project and the instructor has the opportunity to ask you questions about it. Near the end of classes, the instructor will send out a schedule of times during reading period and final exam period that you can use to sign up for a meeting at which you will present your completed project to the instructor. Your code and final paper should be completed and submitted by the start of this meeting. To submit your code and final paper, you should upload them to Gradescope. You needn't submit your dataset, but your final paper should include details of how you acquired your dataset.