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# CSE234: Data Systems for Machine Learning
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## Descriptions
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- Offered by: UCSD
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- Prerequisites: Linear Algebra, Deep Learning, Operating Systems
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- Programming Languages: Python, Triton
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- Difficulty: 🌟🌟🌟
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- Class Hour: 80 hours
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Introduce the course in a paragraph or two, including but not limited to:
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(1) The technical knowledge covered in lectures
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(2) Its differences and features compared to similar courses
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(3) Your personal experiences and feelings after studying this course
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(4) Caveats about studying this course on your own (pitfalls, difficulty warnings, etc.)
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(5) ... ...
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-->
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This course is focused on designing a wholistic LLM System class as an introduction to design efficient systems for LLM.
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The class into three parts, covering the following topics.
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1. Basics: deep learning, autodiff, CUDA programming, ML hardware
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2. ML systems and optimizations: Dataflow graph systems, ML compilation, memory and graph optimization, ML parallelism, auto-parallelization
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3. LLM systems: LLM training, data curation, inference and serving, attention optimization, scaling law, RAG, LLM agents
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## Course Resources
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- Course Website: https://hao-ai-lab.github.io/cse234-w25/
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- Recordings: https://hao-ai-lab.github.io/cse234-w25/
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- Textbooks: https://hao-ai-lab.github.io/cse234-w25/resources/
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- Assignments: https://hao-ai-lab.github.io/cse234-w25/assignments/
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# CSE234: Data Systems for Machine Learning
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## 课程简介
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- 所属大学:UCSD
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- 先修要求:线性代数,深度学习,操作系统
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- 编程语言:Python, Triton
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- 课程难度:🌟🌟🌟
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- 预计学时:80小时
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<!-- 用一两段话介绍这门课程,内容包括但不限于:
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(1)课程覆盖的知识点范围
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(2)与同类课程相比它的优势与特点
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(3)学习这门课程的体验与感受
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(4)自学这门课的注意点(踩过的坑、难度预警等等)
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(5)... ...
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-->
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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。
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课程分为三个部分,涵盖以下主题:
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1. 基础知识:深度学习、自动微分、CUDA编程、机器学习硬件
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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
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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
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## 课程资源
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- 课程网站:https://hao-ai-lab.github.io/cse234-w25/
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- 课程视频:https://hao-ai-lab.github.io/cse234-w25/
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- 课程教材:https://hao-ai-lab.github.io/cse234-w25/resources/
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- 课程作业:https://hao-ai-lab.github.io/cse234-w25/assignments/
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