Table of Contents

• Communication
• Assignments
• Grading
• Final Project
  • Project Proposal
  • Midterm Check-in
  • Final Presentation
  • Final Report
• Paper Reviews
  • Helpful Resources for Reading/Reviewing Papers
• Paper Presentation
• Lab Assignments
• In-Class Discussion
• Extra Credit
• Other Policies

Communication

We’ll be using Piazza as our main source of communication for announcements, questions, discussions, etc. Course notes/slides will be posted on Canvas. Assignments should be uploaded to Gradescope.

Assignments

Assignments for this course will consist of:

• Paper Reading and Summary - Due before each class
• Paper Presentation - Once during the semester
• Two Lab Assignments - Throughout the semester
• Final Project - An open-ended semester-long project consisting of a:
  • Project Proposal
  • Midterm Report
  • Final Presentation
  • Final Report

Grading

• 40% Final Project
  • 5% Project Proposal
  • 5% Midterm Check-in
  • 10% Final Presentation
  • 20% Final Report
• 15% Lab Assignments
• 15% Paper Reviews
• 15% Paper Presentation
• 15% In-class Discussion

Final Project

A major focus of this course is an open-ended, semester-long final project completed by groups of 1-3 students. The project should be broadly related to ML systems, and you are more than welcome to bring your own ideas related to your own research (with a well-defined goal specifically for this course; chat with us if you’re unsure). We’ll also provide a list of potential project ideas. This project is a good way to get started in research beyond this course, so please chat with us if you want to discuss any ideas!

Project Proposal

Deadline: September 23, 2025

Each project group should submit a 1-2 page project proposal with a potential project idea. This report is intended to get your team to identify a project to work on throughout the semester and to get feedback early. If you have multiple potential ideas or are unsure, don’t hesitate to chat with us in advance.

The report should consist of:

• Team members’ names
• Introduction: What is the main motivation behind your project? What is the background needed to understand your project?
• Problem: Clearly define the problem you are trying to solve. What is the key research question your project aims to answer?
• Related Work/Status-Quo: What is the current solution(s) that is used today to solve this problem, and what gaps does it have?
• Key Idea: What is the key idea that your project will leverage to address gaps in current solutions? It’s okay to not be 100% confident that this will work (research is often trial-and-error!), but try to capture the intuition behind your idea/project.
• High-level Plan: Identify some potential tools (systems, frameworks, datasets, infrastructure) that your project plans to use. Give a quick overview of how you plan to use these tools to implement/evaluate your ideas.

Midterm Check-in

Deadline: October 14 during class

Halfway through the semester, we’ll perform a midterm check-in to give you feedback on how your project is going so far. You should plan to have a few (3-5) slides to report what the status of your project is so far, if there are any issues you’re running into, and an outline of what you plan to do for the rest of the semester. You’ll schedule a ~10 minute check-in with us (we’ve reserved the class period for this), where we’ll go over your slides. The goal of the check-in to make sure your project is going in the right direction and to address any issues that have come up.

Final Presentation

Deadline: December 2 or December 4 during class

Each team must present their final project during the last week of class. Plan to have a ~10-15 minute presentation (tentative, depending on the total number of projects). The structure of your presentation should be similar to the paper presentations (see below).

Please submit your presentation on Gradescope on/before the date that you present.

Final Report

Deadline: December 9, 4:30 PM Mountain Time

Each team is expected to write a final report (up to 6 pages without references) following the ACM conference template. The report should be similar to a typical conference/workshop paper and contain:

• Title/team members
• Introduction: What is the motivation for your project? What is the problem you’re trying to solve and why is it important? Give an overview of your project/results, and highlight your main ideas and contributions.
• Background/Motivation: Give requisite background needed for a user to understand your project. What is the current state-of-the-art, and why does it not adequately solve the problem?
• System/Method: Present an overview of your system/architecture (if possible, capture everything in a system diagram). Highlight and provide a detailed discussion of all of the different components that are a part of your project. Discuss the key ideas behind your system. Present how you implemented your work (e.g., what framework, what system, what dataset, etc.). Be sure to clearly distinguish what you implemented if you used existing work as a starting point.
• Evaluation: How did you evaluate your system? What is the setup (datasets, hardware, etc.) you used? What key questions did you seek to answer, and what results help support your answer to those questions?
• Conclusion: Summarize your work in a short paragraph or two.

Paper Reviews

Prior to the start of each class, each student is expected to submit a ½-1 page review (in PDF format) to Gradescope. The goal of this is to get students familiar with the scientific peer review process, which every conference/journal paper goes through prior to publication. Students are expected to act as a peer reviewer for the paper that will be discussed in class.

A good review should:

• Summarize: Summarize the paper in a paragraph or two. Distill out the key problem, motivation, system/solution, insights, and results.
• Evaluate: Evaluate how well the paper did at achieving its goal, both in the significance of the problem (its impact) and the validity and novelty of its solution. Also evaluate the quality of the experimental evaluation and of the manuscript itself.
• Distill: List what your take-aways from reading the paper are
• Highlight strengths: List 1-3 strengths of the paper.
• Highlight weaknesses: List 1-3 things the paper could have done better.
• Discuss: Prepare a discussion topic that you want to bring up during class. This can be anything you want to discuss, from something that was unclear in the paper to its connections to other papers or deployed systems. We’ll use this to drive our discussion during class.

Helpful Resources for Reading/Reviewing Papers

The following are good references to help you get started reading/reviewing systems/CS papers:

• How to Read a Paper
• The Task of the Referee
• Writing Reviews for Systems Conferences

Paper Presentation

Each student will lead a presentation for a paper during a class meeting. We’ll coordinate signups for presentations at the start of the semester. An extremely important part of good research is learning how to communicate your ideas effectively (e.g., at an academic or industry conference). Think of this presentation like you’re presenting your own paper at a conference. You can check out some conference presentations at recent systems conferences for inspiration.

Your presentation should:

• Be 10-15 minutes long.
• Clearly introduce and motivate the problem that the paper is trying to address.
• Present the key ideas introduced in the paper, including an overview of the system (if applicable). Don’t attempt to present everything that the paper talks about. Instead, the audience should understand the intuition behind the paper and its significance. The presentation should make the listener want to go and read the paper.
• Present the results/evaluation of the paper. Did the paper sufficiently address the initial problem? What results support this claim?

Please submit your presentation on Gradescope on/before the date that you present.

Lab Assignments

We will have two lab assignments throughout the course (see deadlines on Schedule). These lab assignments are meant to be relatively lightweight, and their main goal is for you to get familiar with real-world ML system tools and frameworks which you may find useful for your final project.

In-Class Discussion

This is a research-focused course, which involves discussing and analyzing recent research papers. A large portion of each class will be allocated to discussions and debates on the paper/topic for the class. To get the most out of this course, students are expected to actively participate during class discussions. Participation in class discussions, as well as answering in-class Gradescope questions, will factor into the participation portion of your final grade.

Extra Credit

We will likely have more papers than students in the course. In the event that there are un-filled paper presentation slots, students may sign up for an additional paper presentation for an extra 3% credit.

Other Policies

Late Policy

• Paper Presentations, Project Proposals, Midterm Check-ins, Final Presentations, and Final Reports must be submitted on the due date.
• Paper Reviews and Lab Assignments have a total of three (3) late days that can be used across reviews and labs.

Collaboration Policy

• Final projects (and related reports, presentations, etc.) should be done as a team effort within your group.
• While you may discuss Paper Presentations, Reviews, and Lab Assignments with others, they should be submitted individually and should be your own work.

Generative AI Policy

You should treat generative AI analogously to assistance from another person. In other words, you may use generative AI tools (ChatGPT, Cursor, Claude, etc.). However, using generative AI tools to substantially complete an assignment (e.g., directly writing a report/review, completing an assignment) is not permitted. Students should cite/acknowledge extensive use of GenAI tools beyond incidental use, such as light editing, grammar suggestions, and programming aids.

Feedback

We welcome any feedback on how to improve the course! We’ll send periodic forms/polls over the course of the semester for students to provide anonymous feedback. Of course, students are encouraged to reach out to us with any concerns or issues throughout the course.

Honor Code

All students enrolled in a University of Colorado Boulder course are responsible for knowing and adhering to the Honor Code. Violations of the Honor Code may include but are not limited to: plagiarism (including use of paper writing services or technology [such as essay bots]), cheating, fabrication, lying, bribery, threat, unauthorized access to academic materials, clicker fraud, submitting the same or similar work in more than one course without permission from all course instructors involved, and aiding academic dishonesty. Understanding the course’s syllabus is a vital part in adhering to the Honor Code.

All incidents of academic misconduct will be reported to Student Conduct & Conflict Resolution: StudentConduct@colorado.edu. Students found responsible for violating the Honor Code will be assigned resolution outcomes from the Student Conduct & Conflict Resolution as well as be subject to academic sanctions from the faculty member. Visit Honor Code for more information on the academic integrity policy.

Accommodation for Disabilities, Temporary Medical Conditions, and Medical Isolation

If you qualify for accommodations because of a disability, please submit your accommodation letter from Disability Services to your faculty member in a timely manner so that your needs can be addressed. Disability Services determines accommodations based on documented disabilities in the academic environment. Information on requesting accommodations is located on the Disability Services website. Contact Disability Services at 303-492-8671 or DSinfo@colorado.edu for further assistance. If you have a temporary medical condition, see Temporary Medical Conditions on the Disability Services website.

If you have a temporary illness, injury or required medical isolation for which you require adjustment, please email us to discuss.

Accommodation for Religious Obligations

Campus policy requires faculty to provide reasonable accommodations for students who, because of religious obligations, have conflicts with scheduled exams, assignments or required attendance. Please communicate the need for a religious accommodation in a timely manner. In this class, please email us to discuss accommodation options. See the campus policy regarding religious observances for full details.

Preferred Student Names and Pronouns

CU Boulder recognizes that students’ legal information doesn’t always align with how they identify. Students may update their preferred names and pronouns via the student portal; those preferred names and pronouns are listed on instructors’ class rosters. In the absence of such updates, the name that appears on the class roster is the student’s legal name.

Classroom Behavior

Students and faculty are responsible for maintaining an appropriate learning environment in all instructional settings, whether in person, remote, or online. Failure to adhere to such behavioral standards may be subject to discipline. Professional courtesy and sensitivity are especially important with respect to individuals and topics dealing with race, color, national origin, sex, pregnancy, age, disability, creed, religion, sexual orientation, gender identity, gender expression, veteran status, marital status, political affiliation, or political philosophy.

For more information, see the classroom behavior policy, the Student Code of Conduct, and the Office of Institutional Equity and Compliance.

Sexual Misconduct, Discrimination, Harassment and/or Related Retaliation

CU Boulder is committed to fostering an inclusive and welcoming learning, working, and living environment. University policy prohibits protected-class discrimination and harassment, sexual misconduct (harassment, exploitation, and assault), intimate partner abuse (dating or domestic violence), stalking, and related retaliation by or against members of our community on- and off-campus. The Office of Institutional Equity and Compliance (OIEC) addresses these concerns, and individuals who have been subjected to misconduct can contact OIEC at 303-492-2127 or email CUreport@colorado.edu. Information about university policies, reporting options, and OIEC support resources including confidential services can be found on the OIEC website.

Please know that faculty and graduate instructors are required to inform OIEC when they are made aware of incidents related to these concerns regardless of when or where something occurred. This is to ensure that individuals impacted receive outreach from OIEC about their options and support resources. To learn more about reporting and support for a variety of concerns, visit the Don’t Ignore It page.

Mental Health and Wellness

Your health and well-being are the highest priority. Please do not hesitate to reach out to us if there are any circumstances which course staff can be of assistance. Please see below for other resources.

The University of Colorado Boulder is committed to the well-being of all students. If you are struggling with personal stressors, mental health or substance use concerns that are impacting academic or daily life, please contact Counseling and Psychiatric Services (CAPS) located in C4C or call (303) 492-2277, 24/7.

Free and unlimited telehealth is also available through Academic Live Care. The Academic Live Care site also provides information about additional wellness services on campus that are available to students.