Final Project for EEL 4810

1 Option 1: Replication of Experimental or Theoretical Results

For students inclined towards theoretical exploration or experimental validation, this option presents an opportunity to delve into a specific research paper. Your task will involve replicating the experiment results or re-proving the theoretical results outlined in the chosen paper. Upon completion, you are required to compile your results/derivations into a report, detailing the methodology, results, and any insights gained from the process.

2 Option 2: Neural Network Design and Implementation

Alternatively, for those with a preference for practical application and data analysis, you may opt to design and implement a neural network. You will be provided with a training set of PC components, and your task will be to develop a neural network model capable of accurately classifying these components. Your report should document the architecture of your neural network, the training process, performance evaluation metrics, and any optimizations or challenges encountered during implementation.

You can reuse (part of) the code for MNIST dataset uploaded in the course files on Webcourses.

3 Submission Guidelines

Ensure that your final project submission adheres to the following guidelines:

• Deadline: The deadline for submitting your final project is April 30 .

• Report Format: Structure your report logically, including clear sections covering introduction, method- ology, results, discussion, and conclusion. The report should not be more than 5 pages.

• Presentation: Present your findings clearly and concisely, utilizing appropriate visual aids where nec- essary.

• Code: If applicable, include the code you have developed as part of your project. Ensure it is well- commented and organized for easy comprehension.

• Plagiarism: Cite all sources properly and ensure your work is original. Plagiarism will not be tolerated.

4 Evaluation Criteria

Your final project will be evaluated based on the following criteria:

• Depth of Analysis: The extent to which you delve into the chosen topic and the thoroughness of your investigation.

• Clarity of Presentation: The coherence and clarity of your writing and presentation style.

• Creativity and Originality: The degree of creativity and original thinking exhibited in your approach to the project.

• If you choose option 2, the performance of your model on a test dataset will be considered for evaluation.

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