The Mathematical Foundations of Machine Learning

This repository contains the code for the course "The Mathematical Foundations of Machine Learning" at the University of Arkansas. The course is taught by Dr. Jiahui Chen. The course website can be found here.

Getting Started

1. Fork the Repository:

   • Please fork this repository to your own GitLab account.
   • Name your forked repository as MFML-{First Name}-{Last Name}. For example, MFML-John-Doe.
   • This personal repository will be your workspace for all course assignments.

2. Clone Your Forked Repository:

   • Once you have forked the repository, clone it to your local machine to start working on the assignments.
   • Use the command: git clone your-forked-repo-url.

3. Stay Updated:

   • Occasionally, new content or updates might be added to the central MATH-449V-599V-MFML repository.
   • Sync your fork with the central repository regularly to ensure you have the latest updates. Instructions for syncing are provided in the Syncing a Fork guide.

4. Working on Assignments:

   • Complete your assignments within the respective directories in your forked repository.
   • Regularly commit and push your changes to your repository.

5. Submitting Assignments:

   • Ensure that your completed assignments are pushed to your GitLab repository before the deadline.
   • Your submissions will be graded based on the latest commit to your repository before the deadline.

Contents

• Homework: there are 4 homework assignments in this course. Each assignment is a Jupyter notebook with the solutions to the problems and contributes 10% of your final score.
• Projects: the project is a Jupyter notebook with the solutions to the problems and contributes 20% of your final score.
• Labs: contains the 10 labs for this course. Each lab is a Jupyter notebook with the solutions to the problems and contributes 4% of your final score.
• Content: contains the in-class code and content for this course in .ipynb format.
• Code: contains the code for this course in .py format.
• Datasets: contains the data for this course.
  • MNIST: The MNIST dataset is a large database of handwritten digits that is commonly used for training various image processing systems. The dataset is available here.
• License: This repository is licensed under the MIT license. See for details.

Important Links

• Syncing a Fork Guide: Link to guide
• GitLab Markdown Guide: Link to guide

Need Help?

If you encounter any issues or have questions, please refer to the GitLab documentation or reach out on the course forum/discussion board.

Looking forward to a great semester!

Jiahui Chen