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Workshops Folder Overview

Welcome to the Workshops Folder!

This folder is dedicated to the workshops that each student group will prepare and present in class as part of our Advanced Machine Learning course.

What Should You Upload Here?

In this folder, please upload all the materials related to your workshop, including:

  • Workshop Slides: Presentations in formats such as .pptx, .pdf, or any other visual material used during your workshop.
  • Code Examples: Any code, notebooks, or scripts that you will demonstrate as part of your workshop. Please ensure that the code is well-commented and includes instructions on how to run it, if applicable.
  • Workshop Notes/Handouts: Any supplementary materials, such as handouts or reference guides, that you want to share with your peers.
  • README.md (Optional): A brief explanation of your workshop content, including an outline, objectives, and key points of discussion. This will be especially useful for students who want to revisit your material later.

Folder Structure Guidelines:

Please follow these steps to organize your workshop materials:

  1. Create a folder named Workshop_<Topic_Name>, using the name of your workshop topic.
  2. Place all relevant materials (slides, code, notes, etc.) inside this folder. Ensure your files are clearly named and organized for easy reference.
  3. Upload the folder to this repository.

Purpose of the Workshop:

  • Deepen Your Understanding: This is an opportunity for you to explore a specific topic in machine learning in more detail. It will help reinforce your understanding of complex concepts.
  • Teach Your Peers: Sharing your knowledge and teaching others will help solidify your own learning and provide valuable insights for your classmates.
  • Develop Presentation Skills: This workshop is not just about the technical content, but also about developing your ability to communicate and present your ideas clearly and effectively.

Evaluation Criteria:

Your workshop will be evaluated based on the following factors:

  • Clarity of Explanation: How well you explain the topic to your peers.
  • Engagement: How effectively you engage your audience (e.g., through interactive questions, live coding, etc.).
  • Depth of Content: How thoroughly you cover your chosen topic, addressing both theoretical and practical aspects.
  • Quality of Code/Examples: The clarity, structure, and documentation of any code or examples provided.