Introducing Levels of Reproduction and Replication in Machine Learning

Greetings everyone,

I am Mohamed Saeed and I am delighted to be part of the 2023 Summer of Reproducibility program, where I am contributing to the Using Reproducibility in Machine Learning Education project.

My proposal was accepted, and I am fortunate to have Fraida Fund as my mentor. The objective of my project is to develop highly interactive open educational resources that can be utilized by instructors teaching graduate or undergraduate machine learning courses. These resources will focus on integrating instruction on reproducibility and reproducible research principles.

Understanding and practicing reproducibility in machine learning (ML) research is of utmost importance in today’s scientific and technological landscape. Reproducibility ensures the reliability, transparency, and credibility of ML findings and discoveries. By learning the principles of reproducibility, students from different levels can validate research results, test introduced methodologies, and understand level of reproducibilty of research.

My contribution will involve developing interactive educational resources that encompass code examples, writing exercises, and comprehensive explanations of key concepts of reproducing ML research. These resources will be carefully crafted to assist students at various levels of expertise. Our aim is for these resources to be widely adopted by instructors teaching graduate or undergraduate machine learning courses, as they seek to enhance the understanding of reproducibility and reproducible research principles.

I think this is a great opportunity to learn more about ML research reproducibility. I’ll be posting regular updates and informative blogs throughout the summer, so stay tuned!