Using Reproducibility in Machine Learning Education

I am Jonathan Edwin, coming from Indonesia, and I am extremely thrilled to be involved in the 2023 Summer of Reproducibility initiative. I am actively contributing to the project by making valuable contributions to the Using Reproducibility in Machine Learning Education project.

As part of the Using Reproducibility in Machine Learning Education my proposal under the mentorship of Fraida Fund aims to develop educational resources focusing on reproducing and replicating fundamental machine-learning techniques, such as Cutout data augmentation, U-Net, and Siamese networks. The project aims to provide students with a hands-on learning experience that enhances their understanding of the models and their underlying principles while imparting valuable skills in ensuring research reproducibility. The project will involve the creation of a series of interactive Jupyter notebooks covering the selected papers, guiding students through reproducing results, and focusing on best practices for ensuring reproducibility. Upon completion, the notebooks will provide a comprehensive and accessible learning experience for students while emphasizing the importance of reproducibility in machine learning education. The proposal also identifies potential challenges associated with the project and proposed solutions to address them. Challenges include incompatibility issues with the original code and current frameworks or environments, difficulty in reproducing the exact results due to factors such as randomness or lack of specific details in the paper, and ensuring that the interactive elements in the Jupyter Notebooks are engaging and effective in teaching reproducibility concepts.