This repository provides comprehensive lecture notes, slides, and assignments for Professor Hung-yi Lee's Machine Learning courses from 2021-2023. It serves as a valuable resource for students and practitioners seeking to deepen their understanding of modern machine learning concepts, including generative AI, large language models, and diffusion models.

How It Works

The project aggregates materials from Professor Lee's lectures, offering a structured curriculum that covers foundational machine learning principles through advanced topics. It includes direct links to video lectures, presentation slides, and code repositories for assignments, facilitating a hands-on learning experience. The content is organized by topic and year, allowing users to follow the progression of the course material.

Quick Start & Requirements

• Access: All course materials, including lecture videos, slides, and assignment code, are linked within the README.
• Prerequisites: Access to platforms like Bilibili for videos and Kaggle/JudgeBoi for assignments is recommended. Familiarity with Python and deep learning frameworks (PyTorch/TensorFlow) is beneficial for completing assignments.
• Resources: Requires internet access to view videos and download materials. Assignment completion may require a suitable computing environment (e.g., Google Colab, local machine with GPU).

Highlighted Details

• Covers 2021, 2022, and 2023 iterations of the Machine Learning course.
• Detailed breakdown of topics including ChatGPT, diffusion models, and large language models.
• Includes direct links to assignment solutions and submission platforms.
• Curated list of other high-quality AI and ML courses.

Maintenance & Community

The repository is actively maintained, with recent updates in February-May 2023 reflecting new topics and assignments. Community interaction is facilitated through Bilibili and QQ groups.

Licensing & Compatibility

The repository content is presented as authorized by Professor Hung-yi Lee for reproduction and distribution. Specific licensing details for the aggregated materials are not explicitly stated, but the intent appears to be educational sharing.

Limitations & Caveats

The repository primarily serves as a curated collection of external resources; it does not host the course content directly. Users need to access linked platforms for videos and assignments. Some assignment platforms might have specific submission requirements or deadlines.