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Machine Learning Systems
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 quarter 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:
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Fundamentals: Covers topics such as deep learning, automatic differentiation, and an overview of machine learning systems.
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Machine Learning Systems and Optimization: Includes subjects like machine learning compilation, memory and graph optimizations, and distributed machine learning optimization.
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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/
- Course Videos: https://podcast.ucsd.edu/watch/wi25/cse234_a00/1
- Course 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