Class 1: Introduction


Happy New Year!

Welcome to this special-topics course, AI/ML for Networked Systems.


In today’s class, we will spend some time learning more about each other. Please share:

  • Which year of (under) graduate studies are you in at UCSB? What are your plans after graduation?

  • What is your area of research (if any)? What problem are you working on these days (if any)?

  • What motivated you to take this course? What are your expectations from this course?

  • What background do you have in AI/ML or networking? What relevant courses have you taken in the past?

  • Have you taken a special-topics course at UCSB in the past?


Let’s talk about some of the logistics for this class.


I will host office hours between 4-5 pm on Tuesdays/Thursdays, before the class. My office is 1117 Harold Frank Hall (HFH). Please feel free to reach out to me if these timings don’t work for you.


Course website:


Course Expectations

In this course, the students are expected to do the following:

  • Before class: For most classes, we will be reading 1-2 papers. These papers will be assigned at least one week before the class. The students are expected to read the assigned research papers, and submit their reviews using the Google form (more details soon).

  • In-class: Students are expected to participate actively in the class. The instructor will lead the discussions, but the students are expected to elevate the discussions by explaining the key ideas, techniques used, experiments, and summarizing the key takeaways from the paper. Constructively contributing to the discussions, will require students to be prepared by doing the assigned preparation and thinking about the related problems deeply.

  • After class: Students will work in a small team of 2-3 students to summarize the in-class discussions as blogs. For each class, a group will be assigned to produce scribe* blogs* for the discussed topics. For each topic, a team will be assigned to write-up a blog about the topic, which will be posted on the course site.

  • Work in a small team (2-3 students) to work on projects that will contribute to the area of NetAI. Projects may contribute to open source implementations of data collection and analysis tools, ML algorithms, end-2-end systems for data-driven network management, etc. For each project, the students are expected to prepare a project report, which describes the research plan in detail. More details on this later.


You can work in a team for the blogging and research tasks. I will need the name of teams (and its members) by the end of this week (January 10th, 2020). Please keep an eye out for potential team members during the introductions.

Grading Policy

The final grade for the course will be based on the following weights:

  • 40 %: Reviews.

  • 20 %: Class participation.

  • 20 %: Discussions-summary blogs.

  • 20 %: Research project.


Before the start of a lecture, you will submit Google form(s) (typically shared a week in advance). This form will require you to answer some of the questions related to the reading assignments. These questions will test how well you understood the assigned readings. For example, while reviewing a system’s paper, you might have to answer what specific techniques the paper used for evaluation, how the system can be improved, etc. Points will be awarded based on the level of depth demonstrated by your answers.

Writing an insightful review will require effort, so don’t plan to do the reading assignments at the last minute. The quality and depth matter the most, which takes time.

Class Participation

Active in-class participation is desired for this course. Points will be awarded based on your constructive contributions to the topics.

Discussions-summary Blogs

For each class, a team will be assigned to scribe the discussions summaries as blogs. The blogs will be assessed on their structure, clarity, and engagement.

Research Proposal

Your research proposal will be in two parts. First, in the middle of the quarter (see the course schedule for the exact due date), you will submit a preliminary version of your proposal. This initial version should clearly state the problem statement, motivation, and give a rough idea of the related work and critical insights. This version should be at most two pages of content; you may use extra pages for bibliographic references. Your proposal must follow the formatting requirements mentioned above. Your instructor will read your plan in detail and provide feedback, especially on whether the proposed idea has the potential to advance the state-of-the-art. Based on your instructor’s feedback, you will refine your proposal and produce the final version.

Note: The plans that don’t promise to advance the state-of-the-art will receive zero points and ones that show promises of turning into a publication will receive extra credit.

Next Class

In the next class, we will discuss three articles. First, we will examine the report from NSF’s workshop on self-driving networks. This report is your reading assignment for Jan 9th class. Please find the review form below. In addition to this report, we will also dig into the report from the second iteration of the same workshop. Finally, we will discuss a short paper that talks about how to democratize networking research in the era of AI/ML.

The key learning objective is to understand what are self-driving networks, why it makes sense, how we can build them, and what fundamental problems the community needs to solve to make it a reality.

Please find the review forms for the three first article here:

Note: You are only required to fill out the review form for the Self-driving networks workshop report. Reading the other two articles is desired but not required.