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.
Through my undergrad and in my first year of grad school, I had no clue how to do background research. I’m talking finding papers, keep track of what I have read (hopefully with some simple notes or the pdf’s too), and how to find gaps or future directions to investigate. While I am still new to the space and am likely rediscovering the wheel, I struggled finding anyone who laid out their “wheel design” plainly. So, let’s do that here.
As I am putting together more work for the AI exhibit, it is becoming clear just how many pivotal models exist. As the list continues to grow, it only makes sense to make an itemized list of them. See below for what I have collected and the appropriate links I have gathered to understand them.
I grade for a graduate level course in Machine Leasrning. As most people who teach in universities, I have never had any formal training. All my training has come from person experience, what I have been able to pull out of other teachers (themselves not being trained), and research. With this base, the following is a recreation of the “rubric” that I was able to come up with for the assignment:
