CSE 599N: Special Topics (Autumn 2007)
Systems Applications of Machine Learning Techniques
The purpose of this course is to serve as an introduction to the application of machine learning techniques for addressing problems in real-world computer systems, from reliability and performance issues in large-scale systems and networks to power efficiency in sensor networks and self-configuration in complicated systems. The motivation is simple: building empirical models based on statistical pattern recognition, data mining, probabilistic reasoning and other machine learning methods can often help us cope with the challenges of scale and complexity of current and future systems.
Each week there will be readings from recent publications. Most will be drawn broadly from systems conferences, though there will also be occasional readings from machine learning conferences. Students are required to read the papers before class and participate in discussion.
There are no prerequisites---everyone is welcome. Our expectations will vary based on your experience---but those with experience in systems, machine learning, or both will be welcome!
Please feel free to contact us if you have questions about the course in general or about whether you have the appropriate background.
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