Directions I Give Students When Writing Papers
Published:
As we gear up to submit the first project, I have collected common issues student’s have had in previous years. These concepts are reflected in current machine learning research. To get a better grasp on how these expectations all come together to make a complete project, consider reading some recent research papers; however, note that many of these papers have issues and do not deliver on all our expectations. Each project can be seen as a lab you would run in physics or chemistry, and the final paper you submit is the lab report. This works for a computer science course because machine learning is a noisy, application based field, one where empirical tests align well with the problems and the attempted solution (this is very similar to have computer systems and applied algorithms research). Your version of setting up petri dishes, doing titrations, or shining a laser in the right place is setting up a well motivated, cross-validated experiment on datasets. When running your tests, writing your paper, recording your demo, and commenting your code, keep this in mind. What you provide must:
- meet the writing conventions of the field,
- be well formulated, motivated, and defendable,
- be reproducible, assuming hardware and time is available, and
- draw conclusions and state deficiencies of the experiments. How these concepts will be graded can be seen in the rubric attached to each assignment. Please make sure to read it completely, and reach out if you have questions. I also have documents of each rubric that can be cast into an accessible font (OpenDyslexic, Atkinson Hyperlegible, etc.) if you need. Please email me if you need that service.