C: General Guidelines The idea of the group project is to give you some experience trying to do a piece of original research in Data Science and writing up your results in a paper style format. What we expect to see is an idea/task that you describe clearly, relate to existing work, implement and test on a dataset. To do this you will need to write code, run it on some data, make some figures, read a few background papers, collect some references, and write a few pages describing your task, the algorithm(s) you used and the results you obtained. The grade will depend on the ideas, how well you present them in the report, how clearly you position your work relative to existing literature, how illuminating your experiments are, and wellsupported your conclusions are. The idea is that this project report should be a manageable amount of work, but that if you want to turn your project into a paper, everything in the project report will need to be done anyway. If you feel that your project won’t fit into this rubric, please talk to your instructor. There are many ways to make contributions to a field! Your project should consist of an implementation of one or more machine learning algorithms, and their application to a dataset. Your project may be a comparison of several existing algorithms, or it may propose a new algorithm, which should then be compared to at least one other approach. Finding a Data Science project, dataset and applying Data Mining techniques to it can also be of interest. As a under graduate student, you are free to pick a project of your own design. You are free to use any third-party ideas or algorithms that you wish as long as it is publicly available. You must properly provide references to any work that is not your own in the write-up. The project is not intended to be a stressful exercise; instead it is a chance for you to experiment, to think, to play and to hopefully have fun! Start with simple methods that work more or less out of the box and go from there. 3 D. Specific Requirements Length: 4 to 8 pages, not including appendices. Don’t be afraid to keep the text short and to the point, and to include large illustrative figures. 1. Abstract (5 points): a summary of the main idea of the project and its contributions. a. Should be understandable to anyone in the course. b. You don’t need to say everything you did, just say what the main idea was and what were one or two takeaways. 2. Introduction (5 points): Introduce the topic of the project 3. Related work (10 points): A section describing related works and the bibliography. a. If your project builds on previous work, clearly distinguish what they did from what your new contribution is. b. Include a 1-2 sentence summary of other closely related papers. You might not know about all related papers (or have time to carefully read all related papers), and that’s OK for this project. A rough guide is that you should be able to find 3-4 closely related papers, and another 3-4 papers that all those papers cite as foundational work. These foundational papers are often cited in the introduction. 4. Problem Statement (10 points): Include the answer for the following questions: a. Describe how things should work b. Explain the problem and state why it matters c. Explain your problem’s financial costs d. Back up your claims e. Propose a solution f. Explain the benefits of your proposed solutions g. Conclude by summarizing the problem and solution
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