CS446: Machine Learning

Spring 2017

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 5-6 pages in 11 font. Please don't use the guidelines below as subtitles of your paper; these are just guidelines. Try to make it look like a published article. You can download the format files from this webpage (or other conferences) and submit in this format. Make sure to adjust the length if you submit in a two column format.

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?

Dan Roth