Project Guidelines for Student Assignments

You have likely identified your project idea. You may change the idea as the project goes on, but please do check with the professor first. The project is entirely yours so do what you want. Be ambitious! You will not lose marks simply for having poor results (although you may lose marks if your poor results are due to a poor implementation).

Most projects have fallen into an application project, an algorithmic improvement analysis, or a deep analytical dive into a variety of algorithms on a variety of problems. These are all great paths to take but do note that expectations on the analysis side will depend on the particular project.

Any academic misconduct will be investigated fully and I will push for the maximum allowable penalty.

Assuming you get everything working and you don’t have any serious problems, you will automatically get +10 on your assignment.

For the project, modifications is a misnomer. Realistically this is the category I will use to identify what I think is great or lacking about your project. It will likely be a mix of modifications, novelty, analysis, etc. Understand that in this category, like most of the points for the class, I am looking for areas to give you points and not take them away so be sure to (a) give me an excuse to give you points, and (b) make it obvious what you did! You will get points for these things up to a maximum of +10 points.

Again, be sure to make things obvious and convince me that you deserve the extra points. If your improvement is not obvious to me, or if I deem it as not significant enough, you will not get the points.

You can obtain a maximum of +10 for doing a writeup with +8 being from the base report. Unlike the assignments, you are required to do proper references, have figures and tables, and do a proper statistical comparison (most people have failed at this so far in the course).

Please make all the content in the writeup clear and concise. Understand that I want to read as little as possible while understanding as much as possible. Format it however you feel is most effective for communicating to me what you did efficiently.

The writeup is not trivial to do well and will take some time to write. This writeup will be marked more qualitatively by a marker. There is no precise best way to structure a writeup and it is difficult to know exactly what should be included. A portion of these marks will be dedicated to prose,understandability, continuity, spelling, grammar, content, and effectiveness.

Again, do note that the marks for this portion will be more qualitative and it will be difficult to know what’s good beforehand. The content is up for you to descide and your decision making on what to include is part of the assignment and course learning objectives. There is no required length for the report, but please do NOT make it longer than 8 pages double column format.

Note that falsfying results is an academic offense and it will be investigaged fully.

You can obtain +2 if you complete your report in Latex. I will not teach you how to use it, however there are many tutorials available online.

References/Citations 

You must include a sufficient literature review and have proper references/citations. Don’t worry too much about your formatting. There is no correct number of references to include, just do what makes sense in your situation.

If using LaTeX, BibTeX will make your life easier. Google Scholar also REALLY makes your life easy. If you search for an article/book on Scholar, select the blue quote image ( ” ), and then in the bottom of the popup ‘Cite’ window you will see ‘BibTeX’. Click this and copy it into your BibTeX (.bib) file.

Include effective use of figures, tables, etc. in your writeup. Examples include an algorithm flow diagram, a table of parameter settings, tables of results, learning curves, distributions, etc. The marker determines if you receive the mark or not.

Include proper statistics and comparisons. Given the stochastic nature of the algorithm, typically people do at least 30 runs of any experiment (run 30x with the same settings on the same problem instance) to get statistical significance. If you wanted to compare parameter settings, compare the distributions of errors from the 30 runs with two sets of settings. What statistics should you use? What should you actually be comparing? That’s up to you to decide. The marker determines if you receive the mark or not.

Submit via Moodle by 11:55pm on the due data

Include all your code and special running instructions if necessary

Include your writeup

Include anything else you think the marker needs.