- Scribe Duties
- Paper Review Presentation
- Final Project
- Participation Bonus
The class requirements include brief reading summaries, scribe notes for 1 lecture, 4 labs, and a project. This is a PhD level course, and by the end of this class you should have a good understanding of efficient deep learning techniques, and be able to deploy AI applications on resource-constrained devices.
The grading breakdown is as follows:
- Scribe Duties (5%)
- 4 Labs (60%)
- Paper Review Presentation (10%)
- Final Project (25%)
- Participation Bonus (4%)
Note that this class does not have any tests or exams.
Each student is required to scribe for a few lectures. During your assigned lectures, you are to take detailed notes independently. After the lecture, the notes have to be converted into a written markdown (see the guidelines). The notes must be detailed and thorough, and must be submitted through a pull request on GitHub within 1 week after the lecture. TAs will audit and review the submitted notes, request changes if necessary, and will eventually approve the notes.
As long as your scribe notes are complete and accurate, you will be awarded full credit for scribe duties. If your notes have errors or are otherwise not up to standard, we will inform you and give you a chance to correct them.
There will be 4 labs over the course of the semester. These assignments may contain material that has been covered by published papers and webpages. It is a graduate class and we expect students to solve the problems themselves rather than search for answers.
Labs must be done individually: each student must hand in their own answers. However, it is acceptable to collaborate when figuring out answers and to help each other solve the problems. We will be assuming that, as participants in a graduate course, you will be taking the responsibility to make sure you personally understand the solution arising from such collaboration. You also must indicate on each homework with whom you have collaborated.
You will be allowed 6 total homework late days without penalty for the entire semester. You may be late by up to 6 days on any homework assignment. Once those days are used, you will be penalized according to the following policy:
- Homework is worth full credit at the due time on the due date.
- The allowed late days are counted by day (i.e., each new late day starts at 12:00 am ET).
- Once the allowed late days are exceeded, the penalty is 50% per late day conted by day.
- The homework is worth zero credit 2 days after exceeding the late day limit.
You must turn in at least 3 of the 4 assignments, even if for zero credit, in order to pass the course.
If you feel that we have made a mistake in grading your work, please submit a regrading request to TAs during the office hour and we will consider your request. Please note that regrading of a homework may cause your grade to go either up or down.
Paper Review Presentation
The goal of the paper review presentation is to learn how to read papers (critique and extract information).
Every paper will have 2-3 students acting as reviewers, and they should work as a team. Each team is required to present the paper in the class:
- The team will give overview of paper, including background, contributions, methods, and key evaluation results.
- Each student will give strength/weakness of paper.
- The team will answer questions from other students in the class.
The class project will be carried out in groups of 2 or 3 people, and has three main parts: a proposal, a final report, and an oral presentation. The project is an integral part of this class, and is designed to be as similar as possible to researching and writing a conference-style paper.
We appreciate everyone being actively involved in the class! There are several ways of earning participation bonus credit, which will be capped at 4%:
- Piazza participation: The top ~10 contributors to Piazza will get 3%. (To prevent abuse of the system, not all contributions are counted and instructors hold the right to determine to count contributions as positive or negative.)
- Completing mid-semester evaluation: Around the middle of the semester, we will send out a survey to help us understand how the course is going, and how we can improve. Completing it is worth 1%.
- Karma point: Any other act that improves the class, which a TA or instructor notices and deems worthy: 1%.