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ML for Networked Systems

CS 293N, Spring 2022

Course Overview

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.


Almost all lectures will be delivered in-person. We will make the recording for video lectures from last year available on the course website.

  • Lecture
    • Arpit: 9-10:50 pm Tuesday & Thursday, PHELP 2510
  • Office Hours
    • Arpit: 4-5 pm, Friday, HFH 5163
  • Communication