In recent years, we have witnessed the widespread usage of ML tools for various classification, detection, and control problems. More recently, we have witnessed the use of ML for various networking problems as well. However, operationalizing ML solutions for networked systems is more nuanced than simply calibrating existing tools, developed for other domains (image classification, NLP, etc.). More in-depth exploration to develop flexible, scalable, and generalizable ML-based networked systems. In this course, we will cover recent research, published at top networked systems (USENIX NSDI, ACM SIGCOMM) and ML conferences (NeurIPS, ICML, etc.), that developed new ML tools/techniques for networked systems. In the process, we will learn how to identify problems that can (or cannot) benefit from ML, decide which tool/algorithm to use, and how to do interdisciplinary research covering networking, ML, and systems.
There are no official prerequisites; however, a basic familiarity with networking, ML, and distributed systems concepts will be very helpful.
Location: Phelps 3526
Time: 5-6:50 PM, Tuesday and Thursday
Instructor: Arpit Gupta (arpitgupta@cs.ucsb.edu).
My office is 1117 Harold Frank Hall (HFH).
Course website: https://netai-ucsb.github.io. All course materials will be posted on the course website, and students will be expected to provide materials to add to this site.
Slack: https://netai-ucsb.slack.com. We will use a slack group for class communications. You can join using any @ucsb.edu email address. You can also create slack channels for your team communications.
This schedule is tentative. Please refer to this page to get the most recent version of the schedule.