Below are guidelines on how to write-up your final report for the project. Of course, for most projects only a subset of the comments below are relevant and for some project other structures could be more appropriate. However, you can use it as a general guide in structuring your final report.
A "standard" experimental machine learning paper consists of the following sections:
1. Introduction
Motivate and abstractly describe the problem you are addressing and how you
are addressing it. What is the problem? Why is it important? What is your basic
approach? A short discussion of how it fits into related work in the area is
also desirable. Summarize the basic results and conclusions that you will
present.
2. Problem Definition and Algorithm
2.1 Task
Definition
Precisely define the problem you are addressing (i.e. formally specify the
inputs and outputs). Elaborate on why this is an interesting and important
problem.
2.2 Algorithm Definition
Describe in reasonable detail the algorithm you are using to address this problem. A pseudo-code description of the algorithm you are using may be useful. If it makes sense for your project, illustrate your algorithm using an example. The example should be complex enough to illustrate all of the important aspects of the problem but simple enough to be easily understood. If possible, an intuitively meaningful example is better than one with meaningless symbols.
3. System Design
Describe how you implemented your system and how you structured it. This should give an overview of the system, not a detailed documentation of the code. The documentation of the code is part of the code you hand in. You might want to comment on high-level design decisions that your made.
3. Experimental (or Theoretical) Evaluation
3.1 Methodology
What are the criteria you are using to evaluate your method? What specific
hypotheses does your experiment test? Describe the experimental methodology that
you used. What are the dependent and independent variables? What is the training/test data that was used, and why is it
realistic or interesting? Exactly what performance data did you collect and how
are you presenting and analyzing it? How did you select the parameters of your
method? What is the baseline performance of a system that does not learn (e.g.
always predicts the majority class or predicts randomly). Comparisons to competing methods that
address the same problem or to variations of your own algorithm are particularly
useful.
3.2 Results
Present the quantitative results of your experiments. Graphical data
presentation such as graphs and histograms are frequently better than tables.
What are the basic differences revealed in the data.
3.3 Discussion
Is your hypothesis supported? Are the results statistically significant? What conclusions do the results support about
the strengths and weaknesses of your method compared to other methods? How can
the results be explained in terms of the underlying properties of the algorithm
and/or the data.
4. Related Work
Can you say anything about related work from your background readings? It may
be possible to answer the following questions for each piece of related work
that addresses the same or a similar problem. What is their problem and method?
How is your problem and method different? Why is your problem and method better?
5. Future Work
What are the major shortcomings of your current method? For each shortcoming,
propose additions or enhancements that would help overcome it.
6.
Conclusion
Briefly summarize the important results and conclusions
presented in the paper. What are the most important points illustrated by your
work? How will your results improve future research and applications in the
area?
APPENDIX:
Source code of your system
submitted via CMS.