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35 changes: 35 additions & 0 deletions docs/机器学习系统/CSE234.en.md
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# CSE234: Data Systems for Machine Learning

## Descriptions

- Offered by: UCSD
- Prerequisites: Linear Algebra, Deep Learning, Operating Systems
- Programming Languages: Python, Triton
- Difficulty: 🌟🌟🌟
- Class Hour: 80 hours

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This course is focused on designing a wholistic LLM System class as an introduction to design efficient systems for LLM.

The class into three parts, covering the following topics.

1. Basics: deep learning, autodiff, CUDA programming, ML hardware
2. ML systems and optimizations: Dataflow graph systems, ML compilation, memory and graph optimization, ML parallelism, auto-parallelization
3. LLM systems: LLM training, data curation, inference and serving, attention optimization, scaling law, RAG, LLM agents


## Course Resources

- Course Website: https://hao-ai-lab.github.io/cse234-w25/
- Recordings: https://hao-ai-lab.github.io/cse234-w25/
- Textbooks: https://hao-ai-lab.github.io/cse234-w25/resources/
- Assignments: https://hao-ai-lab.github.io/cse234-w25/assignments/
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# CSE234: Data Systems for Machine Learning


## 课程简介

- 所属大学:UCSD
- 先修要求:线性代数,深度学习,操作系统
- 编程语言:Python, Triton
- 课程难度:🌟🌟🌟
- 预计学时:80小时

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本课程专注于设计一个全面的大语言模型(LLM)系统课程,作为设计高效LLM系统的入门介绍。

课程分为三个部分,涵盖以下主题:

1. 基础知识:深度学习、自动微分、CUDA编程、机器学习硬件
2. 机器学习系统与优化:数据流图系统、机器学习编译、内存与图优化、机器学习并行化、自动并行化
3. 大语言模型系统:LLM训练、数据整理、推理与服务、注意力机制优化、缩放定律、检索增强生成(RAG)、Agent


## 课程资源

- 课程网站:https://hao-ai-lab.github.io/cse234-w25/
- 课程视频:https://hao-ai-lab.github.io/cse234-w25/
- 课程教材:https://hao-ai-lab.github.io/cse234-w25/resources/
- 课程作业:https://hao-ai-lab.github.io/cse234-w25/assignments/