add cse 234 w25

docs/机器学习系统/MLSYS.en.md

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+# Data Systems for Machine Learning
+
+## Course Overview
+
+- University: University of California, San Diego
+- Prerequisites: Fundamentals of Deep Learning and Computer Systems
+- Programming Language: Python
+- Course Difficulty: 🌟🌟🌟
+- Estimated Study Hours: 50 hours
+
+This course, offered in the Winter 2025 term by Professor Hao Zhang at the University of California, San Diego, focuses on machine learning systems, encompassing the latest research developments in large (langauge) models, machine learning compilation, and distributed systems.
+
+The curriculum is divided into three main sections:
+
+1. **Fundamentals**: Covers topics such as deep learning, automatic differentiation, and an overview of machine learning systems.
+
+2. **Machine Learning Systems and Optimization**: Includes subjects like dataflow graph systems, machine learning compilation, memory and graph optimizations, and distributed machine learning optimization.
+
+3. **Large (Language) Models**: Explores cutting-edge topics such as training of large language models (LLMs), data preparation, inference and serving, attention mechanism optimization, and retrieval-augmented generation (RAG).
+
+The course also features guest lectures from inventors of key technologies and industry leaders, providing students with direct interaction opportunities with experts. A foundation in deep learning and system programming is needed for this course. It offers extensive programming assignments and reading materials to help students deeply understand the design and optimization of machine learning systems. Self-learners should be aware that the course involves a significant amount of cutting-edge research, which may require additional time to consult related materials for a thorough understanding.
+
+## Course Resources
+
+- **Course Website**: [https://hao-ai-lab.github.io/cse234-w25/](https://hao-ai-lab.github.io/cse234-w25/)
+- **Course Videos**: [https://podcast.ucsd.edu/watch/wi25/cse234_a00/1](https://podcast.ucsd.edu/watch/wi25/cse234_a00/1)
+- **Course Notes**: [https://github.com/hao-ai-lab/cse234-w25/tree/main/assets/scribe_notes](https://github.com/hao-ai-lab/cse234-w25/tree/main/assets/scribe_notes)
+- **Course Assignments**: [https://github.com/hao-ai-lab/cse234-w25-PA](https://github.com/hao-ai-lab/cse234-w25-PA)

docs/机器学习系统/MLSYS.md

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+# Data Systems for Machine Learning
+
+## 课程简介
+
+- 所属大学：加州大学圣迭戈分校
+- 先修要求：深度学习基础/计算机系统基础
+- 编程语言：Python
+- 课程难度：🌟🌟🌟
+- 预计学时：50小时
+
+<!-- 用一两段话介绍这门课程，内容包括但不限于：
+    （1）课程覆盖的知识点范围
+    （2）与同类课程相比它的优势与特点
+    （3）学习这门课程的体验与感受
+    （4）自学这门课的注意点（踩过的坑、难度预警等等）
+    （5）... ...
+-->
+
+这门课程由机器学习系统领域顶尖学者，来自加州大学圣迭戈分校的张昊教授于2025年冬季学期开设，聚焦于机器学习系统，涵盖大模型、机器学习编译和分布式系统等领域的最新研究进展。
+
+课程内容分为三个部分：
+
+1. 基础知识：​包括深度学习、自动微分、机器学习系统概述等。
+
+2. 机器学习系统与优化：​涵盖数据流图系统、机器学习编译、内存与图优化、分布式机器学习优化等主题。
+
+3. 大（语言）模型：​探讨LLM的训练、数据准备、推理与服务、注意力机制优化、检索增强生成（RAG）等前沿话题。​
+
+课程还邀请了多位关键技术的发明者和行业领军人物进行客座讲座，为学生提供与行业专家直接交流的机会。学习这门课程需要具备在深度学习和系统编程扎实的编程基础。​课程提供了丰富的编程作业和阅读材料，有助于学生深入理解机器学习系统的设计与优化。​自学者应注意，课程内容涉及大量前沿研究，可能需要额外时间查阅相关资料以加深理解。
+
+## 课程资源
+
+- 课程网站：https://hao-ai-lab.github.io/cse234-w25/
+- 课程视频：https://podcast.ucsd.edu/watch/wi25/cse234_a00/1
+- 课程笔记：https://github.com/hao-ai-lab/cse234-w25/tree/main/assets/scribe_notes
+- 课程作业：https://github.com/hao-ai-lab/cse234-w25-PA