Project Report |

** The final project report is due on May 11 2017. **

Below are guidelines on how to write up your report for the final
project. **These are only guidelines; you will need to adjust it to the
problem you are investigating, but try to structure your report along
these suggestions and make it look like an article.**
The length of the report should be

**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 approach?
What is the goal of your paper?

Provide a short discussion of how it fits into related work in the area.

Summarize the basic results, conclusions and contributions that you will
present.
All these apply equally to experimental papers, survey papers or
theoretical papers.

**2. Problem Definition and Algorithms**

2.1 Task Definition

Introduce the model and/or problem you are studying and define the
notation you are going to use. Precisely define the problem you are
addressing (e.g., formally specify the inputs and outputs).
Elaborate on why this is an interesting and important problem.

2.2 Algorithm Definition

If you study learning algorithm(s) experimentally this is the place to
present it. Describe in reasonable details the algorithm(s) you are
using. A pseudo-code description of the algorithm you are using is
frequently useful. Depending on the context, it may be useful to trace
through a concrete example, showing how your algorithm processes this
example.

2.3 Expectations

In case of an experimental study, discuss what you hope to achieve.
How do you expect each algorithm to behave and why. Try to justify your
hypothesis as rigorously as possible. Discuss how your expectations
drive your experimental design.

**3(i). Experimental Evaluation**

3.1 Methodology

What are the criteria you are using to evaluate your method? Describe the
experimental methodology that you used. What is the training/test
data that was used, and why is it realistic or interesting? What
performance data did you collect and how are you presenting and
analyzing it?

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. Are they statistically
significant?

3.3 Discussion

Is your hypothesis supported? 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.

**3(ii). Theoretical Evaluation**

If you are writing
a theoretical paper and/or a survey, this is the place for your
analysis and contribution. Try to make it clear what parts of the work
are presentation of known work, what is given a new look by your
presentation and what is novel in your view of the problem.

**4. Related Work**

This part need not be exhaustive, but you need to know about some of
the related work. Discuss the problem and method in the related work.
How is your problem and method different? Why is your problem and
method better?

**5. Future Work**

(Only if relevant)

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?