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Kimi-K3
Kimi K3 is an open-weight, 2.8T-parameter native multimodal agentic model with a 1M-token context window, designed for frontier coding, knowledge work, and reasoning tasks.
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FR-AI / ANALYSISWhy it is worth attention
It provides a well-structured, downloadable collection of slides from two years of a widely respected ML course, including some homework with code and data, making it a valuable study resource.
Who it is for
- Machine learning students following Li Hongyi's course
- Self-learners seeking organized lecture materials
- Educators looking for reference slides
- Researchers reviewing ML topics
Use cases
- Offline study or review of specific lecture topics
- Quick access to slides for homework or exam preparation
- Reference for teaching or preparing ML courses
- Reproducing homework results with provided code and data
Strengths
- Lectures from both 2021 and 2022 are grouped by year and lecture number for easy navigation
- Includes 2022 homework (HW01–HW03) with code, data, and results
- Uses Git LFS to manage large PPTX files without bloating the repository
- Provides a CSV file linking each lecture file to its original download source
Considerations
- Not an official repository; some lectures (e.g., 2022 Lecture 04) have no PDF provided on the course page
- Only a subset of homework (2022 HW01–HW03) is included; 2021 homework is not archived
- Large files require Git LFS setup, which may be an extra step for some users
README quick start
李宏毅《机器学习》2021 / 2022 Spring 课程资料
本仓库按课程年份和 Lecture 顺序整理李宏毅老师 Machine Learning 2021 Spring 与 Machine Learning 2022 Spring 课程页面公开提供的 PDF / PPTX 讲义，方便个人学习与查阅。
本仓库是个人学习归档，并非课程官方仓库。课程安排与资料版本请以课程主页为准。
快速导航
2021 Spring 讲义目录 · 2022 Spring 讲义目录 · 2022 作业目录 · Git LFS · 版权说明
目录结构
.
├── 2021/
│ ├── lectures/ # 13 个 Lecture 分类，45 份 PDF / PPTX
│ └── lecture-files.csv # 文件名、Lecture 与原始下载地址
└── 2022/
├── lectures/ # 原仓库的 2022 课程讲义
└── homeworks/ # 原仓库已有作业，当前包含 HW01
2021 Spring
| Lecture | 课程主题 | 主要讲义 |
|---|---|---|
| Lecture 01 | Introduction | 课程介绍（中/英文）、Regression |
| Lecture 02 | Deep Learning | Overfit、Critical Point、Optimizer、Classification |
| Lecture 03 | CNN & Self-Attention | ML Pretest、CNN、Self-Attention |
| Lecture 04 | Theory of ML | PAC Learning |
| Lecture 05 | Transformer | Normalization、Seq2Seq |
| Lecture 06 | Generative Model | GAN |
| Lecture 07 | Self-Supervised Learning | BERT、Auto-Encoder |
| Lecture 08 | Explainable AI / Adversarial Attack | XAI、Adversarial Attack |
| Lecture 09 | Domain Adaptation | Domain Adaptation |
| Lecture 10 | Reinforcement Learning | DRL |
| Lecture 11 | Quantum ML | 客座讲义 |
| Lecture 12 | Life-Long Learning / Compression | Life-long Learning、Network Compression |
| [Lecture 13](./2021/lectures/Lecture%2013%20-%20Meta%20Lear |
Description
2022 Spring 李宏毅机器学习课程笔记、作业与实验
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