Coursework
Disclaimer: Coursework is tentative and subject to change!
Assignments (48%)
There are four weekly assignments, which will improve both your theoretical understanding and your practical skills. All assignments contain both written questions and programming parts. In office hours, TAs may look at students’ code for assignments 1 and 2, but not for assignments 3 and 4.
- Credit:
- Assignment 1 (6%): Introduction to word vectors
- Assignment 2 (14%): Neural network foundations, calculating tensor derivatives, dependency parsing
- Assignment 3 (14%): Neural Machine Translation with sequence-to-sequence, attention, and subwords
- Assignment 4 (14%): Self-supervised learning and fine-tuning with Transformers
- Deadlines: All assignments are due on either a Tuesday or a Thursday before class (i.e. before 4:30pm). All deadlines are listed in the schedule.
- Submission: Assignments are submitted via Gradescope. You will be able to access the course Gradescope page on Canvas. If you need to sign up for a Gradescope account, please use your @stanford.edu email address. Further instructions are given in each assignment handout.
Do not email us your assignments.
- Late start: If the result gives you a higher grade, we will not use your assignment 1 score, and we will give you an assignment grade based on counting each of assignments 2–4 at 16%.
- Collaboration:
Study groups are allowed, but students must understand and complete their own assignments, and hand in one assignment per student.
If you worked in a group, please put the names of the members of your study group at the top of your assignment.
Please ask if you have any questions about the collaboration policy.
- Honor Code:
We expect students to not look at solutions or implementations online. Like all other classes at Stanford, we take the student Honor Code seriously. We sometimes use automated methods to detect overly similar assignment solutions.
Final Project (49%)
The Final Project offers you the chance to apply your newly acquired skills towards an in-depth application.
Students have two options: the Default Final Project (in which students tackle a predefined task, namely implementing a minimalist version of BERT) or a Custom Final Project (in which students choose their own project involving human language and deep learning). Examples of both can be seen on last year's website. Note: TAs may not look at students' code for either the default or custom final projects.
Important information
- Credit: For both default and custom projects, credit for the final project is broken down as follows:
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Project proposal (8%)
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Project milestone (6%)
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Project poster (3%)
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Project report (32%)
- Deadlines: The project proposal, milestone and report are all due at 4:30pm. All deadlines are listed in the schedule.
- Default Final Project: In this project, students implement parts of the BERT architecture and use it to tackle 3 downstream tasks.
Similar to previous years, the code is in PyTorch.
- Project advice [lecture slides] [custom project tips]: The Practical Tips for Final Projects lecture provides guidance for choosing and planning your project.
To get project advice from staff members, first look at each staff member's areas of expertise on the office hours page. This should help you find a staff member who is knowledgable about your project area.
- Ethics-related questions: For guidance on projects dealing with ethical questions, or ethical questions that arise during your project, please contact Wanheng Hu (wanhenghu@stanford.edu) or Veronica Rivera (varivera@stanford.edu).
Practicalities
- Team size: Students may do final projects solo, or in teams of up to 3 people. We strongly recommend you do the final project in a team. Larger teams are expected to do correspondingly larger projects, and you should only form a 3-person team if you are planning to do an ambitious project where every team member will have a significant contribution.
- Contribution: In the final report we ask for a statement of what each team member contributed to the project. Team members will typically get the same grade, but we may differentiate in extreme cases of unequal contribution. You can contact us in confidence in the event of unequal contribution.
- External collaborators: You can work on a project that has external (non CS224N student) collaborators, but you must make it clear in your final report which parts of the project were your work.
- Sharing projects: You can share a single project between CS224N and another class, but we expect the project to be accordingly bigger, and you must declare that you are sharing the project in your project proposal.
- Mentors: Every custom project team has a mentor, who gives feedback and advice during the project. Default project teams do not have mentors. A project may have an external (i.e., not course staff) mentor; otherwise, we will assign a CS224N staff mentor to custom project teams after project proposals.
- Computing resources: All teams will receive credits to use Google Cloud Platform, thanks to a kind donation by Google!
- Using external resources: The following guidelines apply to all projects (though the default project has some more specific rules, details provided in the Honor Code section of the handout):
- You can use any deep learning framework you like (PyTorch, TensorFlow, etc.)
- More generally, you may use any existing code, libraries, etc. and consult any papers, books, online references, etc. for your project. However, you must cite your sources in your writeup and clearly indicate which parts of the project are your contribution and which parts were implemented by others.
- Under no circumstances may you look at another CS224N group's code, or incorporate their code into your project.
Participation (3%)
We appreciate everyone being actively involved in the class! There are several ways of earning participation credit, which is capped at 3%:
- Attending guest speakers' lectures:
- In the second half of the class, we have five invited speakers.
Our guest speakers make a significant effort to come lecture for us, so
(both to show our appreciation and to continue attracting interesting speakers)
we do not want them lecturing to a largely empty room.
As such, we encourage students to attend these virtual lectures live, and participate in Q&A.
- All students get 0.3% per speaker (1.5% total) for either attending the guest lecture in person, or by writing a reaction paragraph if you watched the talk remotely; details will be provided.
Students do not need to attend lecture live to write these reaction paragraphs; they may watch asynchronously.
- Completing feedback surveys: We will send out two feedback surveys (mid-quarter and end-of-quarter) to help us understand how the course is going, and how we can improve. Each of the two surveys are worth 0.5%.
- Ed participation: The top ~20 contributors to Ed will get 3%; others will get credit in proportion to the participation of the ~20th person.
- Karma point: Any other act that improves the class, like helping out another student in office hours or writing a useful guide for students on some topic, which a CS224N TA or instructor notices and deems worthy: 1%
Late Days
- Each student has 6 late days to use. A late day extends the deadline 24 hours. You can use up to 3 late days per assignment (including all four assignments, project proposal, project milestone and project final report).
- Final project teams can share late days between members. For example, a group of three people must have at least six late days between them to extend the deadline by two days. If any late days are being shared, this must be clearly marked at the beginning of the report, and we will release a form on Ed that teams should fill out..
- Once you have used all 6 late days, the penalty is 1% off the final course grade for each additional late day.
Regrade Requests
If you feel you deserved a better grade on an assignment, you may submit a regrade request on Gradescope within 3 days after the grades are released.
Your request should briefly summarize why you feel the original grade was unfair.
Your TA will reevaluate your assignment as soon as possible, and then issue a decision.
If you are still not happy, you can ask for your assignment to be regraded by an instructor.
Credit/No credit enrollment
If you take the class credit/no credit then you are graded in the same way as those registered for a letter grade. The only difference is that, providing you reach a C- standard in your work, it will simply be graded as CR.
All students welcome
We are committed to doing what we can to work for equity and to create an inclusive learning environment that actively values the diversity of backgrounds, identities, and experiences of everyone in CS224N. We also know that we will sometimes make missteps. If you notice some way that we could do better, we hope that you will let someone in the course staff know about it.
Well-Being and Mental Health
If you are experiencing personal, academic, or relationship problems and would like to talk to someone with training and experience, reach out to the Counseling and Psychological Services (CAPS) on campus. CAPS is the university’s counseling center dedicated to student mental health and wellbeing. Phone assessment appointments can be made at CAPS by calling 650-723-3785, or by accessing the VadenPatient portal through the Vaden website.
Auditing the course
In general we are happy to have auditors if they are a member of the Stanford community (registered student, official visitor, staff, or faculty). If you are interested, email us at cs224n-win2425-staff@lists.stanford.edu. If you want to actually master the material of the class, we very strongly recommend that auditors do all the assignments. However, due to high enrollment, we cannot grade the work of any students who are not officially enrolled in the class.
Students with Documented Disabilities
We assume that all of us learn in different ways, and that the organization of the course must accommodate each student differently. We are committed to ensuring the full participation of all enrolled students in this class.
If you need an academic accommodation based on a disability, you should initiate the request with the Office of Accessible Education (OAE).
The OAE will evaluate the request, recommend accommodations, and prepare a letter for faculty. Students should contact the OAE as soon as possible and at any rate in advance of assignment deadlines, since timely notice is needed to coordinate accommodations. Students should also send your accommodation letter to either the staff mailing list (cs224n-win2425-staff@lists.stanford.edu) or make a private post on Ed, as soon as possible.
OAE accommodations for group projects: OAE accommodations will not be extended to collaborative assignments.
AI Tools Policy
Students are required to independently submit their solutions for CS224N homework assignments. Collaboration with generative AI tools such as Co-Pilot and ChatGPT is allowed, treating them as collaborators in the problem-solving process. However, the direct solicitation of answers or copying solutions, whether from peers or external sources, is strictly prohibited.
Employing AI tools to substantially complete assignments or exams will be considered a violation of the Honor Code. For additional details, please refer to the Generative AI Policy Guidance here.
Sexual violence
Academic accommodations are available for students who have experienced or are recovering from sexual violence. If you would like to talk to a confidential resource, you can schedule a meeting with the Confidential Support Team or call their 24/7 hotline at: 650-725-9955. Counseling and Psychological Services also offers confidential counseling services. Non-confidential resources include the Title IX Office, for investigation and accommodations, and the SARA Office, for healing programs. Students can also speak directly with the teaching staff to arrange accommodations. Note that university employees – including professors and TAs – are required to report what they know about incidents of sexual or relationship violence, stalking and sexual harassment to the Title IX Office. Students can learn more at https://vaden.stanford.edu/sexual-assault.
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