machine learning project

Machine Learning

Project Report Format

(Source : https://www.cs.utexas.edu/~mooney/cs391L/paper-template.html)

Below are guidelines on how to write-up your report for the final project. Of course, for a short

class project, all of the comments may not be relevant. However, please 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 psuedocode

description of the algorithm you are using is frequently useful. Trace through a concrete

example, showing how your algorithm processes this 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. Experimental Evaluation

3.1 Methodology

What are 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? Comparisons to competing methods that address the same problem 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? 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.

4. Related Work

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?

Bibliography

Be sure to include a standard, well-formatted, comprehensive bibliography with citations from

the text referring to previously published papers in the scientific literature that you utilized or are

related to your work.

Other sources:

- Depository: https://archive.ics.uci.edu/ml/index.php

- Depository: https://kaggle.com - Project Format: https://www.kdnuggets.com/2018/12/machine-learning-project-checklist.html