Course Syllabus

Table of contents

1. Reading
2. Review
3. Presentation
4. Class Participation
5. Late policy
6. Grading
7. Academic Integrity
8. Students with disabilities or learning needs

Reading

There are no textbooks. Required readings are mostly in the form of seminal research papers on serverless computing and AI systems. Occasionally they come from online documentations.

There are several books that might be useful:

• Distributed Systems 3rd edition (2017), by Maarten van Steen and Andrew S. Tenenbaum (free book).

• Operating Systems: Three Easy Pieces (OSTEP), by Remzi H. Arpaci-Dusseau and Andrea C. Arpaci-Dusseau, Aug, 2018 v 1.00 (free book).

Review

Students are required to review assigned readings thoroughly. These reading materials will be presented and discussed in class.

This class uses the following review protocol:

• Reading: During each class, we will discuss two papers. You should read these two papers before the class, making sure to understand the key ideas, designs, findings, and conclusions.

• Review: Prepare a one-page review for one of the papers based on your choice. (You should read both papers though.) Summarize the main ideas, arguments, your own reflections, and any questions that you have. This review needs to be submitted before the class. You are allowed to miss at most 5 reviews without receiving penalty.

• Note for presentor: If you are presenting this paper in the class, you don’t need to submit the review for this class (this is not counted as missing reviews).

• AI-tool policy for paper reviews: While we acknowledge the convenience of AI writing assistants like ChatGPT in the academic domain, it is crucial for students to cultivate their critical thinking and writing skills. The review should reflect your own experience and insight into the content. Students may use AI tools for brainstorming initial ideas or checking grammar errors, but the final reviews submitted must be authored by the student. Students are expected to elaborate their reviews in the class.

Presentation

Each student will be assigned multiple research papers (based on class size) to present during the semester.

• Preparation: Prepare a 25-30 minutes presentation on the paper, summarizing the research problem, methods, results, and implications. Please send the slides to the instructor the week before the presentation.

• Discussion: Be prepared to answer questions and lead a discussion about the paper after your presentation.

Class Participation

• Attendance: Regular attendance is expected and will be taken into consideration when grading class participation.

• Engagement: You are expected to actively participate in class discussions by asking questions, sharing your thoughts, responding to your classmates’ ideas, and contributing to an inclusive and respectful classroom environment.

• Peer feedback: Provide constructive feedback during your peers’ presentations and contribute to post-presentation discussions.

Late policy

Late policy applies differently to review submissions and project submissions.

• For review submissions: We do not accept late review submissions as you already get 5 wild cards.

• For project submissions: Your work is late if it is not turned in by the deadline.

  • 10% will be deducted for late submissions each day after the due date. That is, if an assignment is late, we’ll grade it and scale the score by 0.9 if it is up to one day late, by 0.8 if it is up to two days late, and by 0.7 if it is up to three days late.
  • Late submissions will only be accepted for 3 days after the due date. Assignments submitted more than 3 days late will receive a zero. If you’re worried about being busy around the time of a HW submission, please plan ahead and get started early.
  • Assignment that does not compile or run will receive at most 50% credit.

IMPORTANT: Please plan ahead and get started early! Debugging distributed systems can be time-consuming, even with AI’s help.

For fairness to all, there are no exceptions to the above rules.

Grading

Your grade will be calculated as follows:

• Reviews: 10%
• Class participation: 10%
• Presentation: 30%
• Project: 50%

Academic Integrity

The School relies upon and cherishes its community of trust. We firmly endorse, uphold, and embrace the University’s Honor principle that students will not lie, cheat, or steal, nor shall they tolerate those who do. We recognize that even one honor infraction can destroy an exemplary reputation that has taken years to build. Acting in a manner consistent with the principles of honor will benefit every member of the community both while enrolled in the School of Data Science and in the future. Students are expected to be familiar with the university honor code, including the section on academic fraud.

Students with disabilities or learning needs

It is my goal to create a learning experience that is as accessible as possible. If you anticipate any issues related to the format, materials, or requirements of this course, please meet with me outside of class so we can explore potential options. Students with disabilities may also wish to work with the Student Disability Access Center to discuss a range of options to removing barriers in this course, including official accommodations. Please visit their website for information on this process and to apply for services online. If you have already been approved for accommodations through SDAC, please send me your accommodation letter and meet with me so we can develop an implementation plan together.