CS229 Final Project Information

One of CS229's main goals is to prepare you to apply machine learning algorithms to real-world tasks, or to leave you well-qualified to start machine learning or AI research. The final project is intended to start you in these directions.

For group-specific questions regarding projects, please create a private post on Piazza. Please first have a look through the frequently asked questions.
Note: only one group member is supposed to submit the assignment, and tag the rest of the group members (do not all submit separately, or on the flip side forget to tag your teammates if you are the group's designated submitter). If you do not do this, you can submit a regrade request and we will fix it, but we will also deduct 1 point.

Previous projects

2019 (Autumn) 2019 (Spring) 2018 2017 2016 2016 (Spring) 2015
2014 2013 2012 2011 2010 2009
2008 2007 2006 2005 2004

Project Topics

Your first task is to pick a project topic. If you're looking for project ideas, please come to project office hours, and we'd be happy to brainstorm and suggest some project ideas. In the meantime, here are some suggestions that might also help.

Most students do one of three kinds of projects:

  1. Application project. This is by far the most common: Pick an application that interests you, and explore how best to apply learning algorithms to solve it.
  2. Algorithmic project. Pick a problem or family of problems, and develop a new learning algorithm, or a novel variant of an existing algorithm, to solve it.
  3. Theoretical project. Prove some interesting/non-trivial properties of a new or an existing learning algorithm. (This is often quite difficult, and so very few, if any, projects will be purely theoretical.)
Some projects will also combine elements of applications, algorithms and theory.

Many fantastic class projects come from students picking either an application area that they're interested in, or picking some subfield of machine learning that they want to explore more. So, pick something that you can get excited and passionate about! Be brave rather than timid, and do feel free to propose ambitious things that you're excited about. (Just be sure to ask us for help if you're uncertain how to best get started.) Alternatively, if you're already working on a research or industry project that machine learning might apply to, then you may already have a great project idea.

A very good CS229 project will be a publishable or nearly-publishable piece of work. Each year, some number of students continue working on their projects after completing CS229, submitting their work to a conferences or journals. Thus, for inspiration, you might also look at some recent machine learning research papers. Two of the main machine learning conferences are ICML and NIPS. You can find papers from the recent ICML https://icml.cc/Conferences/2019/Schedule and NeurIPS conference https://neurips.cc/Conferences/2019/Schedule. Finally, looking at class projects from previous years is a good way to get ideas.

Once you have identified a topic of interest, it can be useful to look up existing research on relevant topics by searching related keywords on an academic search engine such as: http://scholar.google.com. Another important aspect of designing your project is to identify one or several datasets suitable for your topic of interest. If that data needs considerable pre-processing  to suit your task, or that you intend to collect the needed data yourself, keep in mind that this is only one part of the expected project work, but can often take considerable time. We still expect a solid methodology and discussion of results, so pace your project accordingly.

Notes on a few specific types of projects:

Project Parts: Proposal, Milestone, Poster, & Final Report

This section contains the detailed instructions for the different parts of your project.

Submission: We’ll be using Gradescope for submission of all four parts of the final project. We’ll announce when submissions are open for each part. You should submit on Gradescope as a group: that is, for each part, please make one submission for your entire project group and tag your team members.


We will not be disclosing the breakdown of the 40% that the final project is worth amongst the different parts, but the poster and final report will combine to be the majority of the grade. Projects will be evaluated based on:

In order to highlight these components, it is important you present a solid discussion regarding the learnings from the development of your method, and summarizing how your work compares to existing approaches.

Project Proposals

In the project proposal, you'll pick a project idea to work on early and receive feedback from the TAs. If your proposed project will be done jointly with a different class' project, you should obtain approval from the other instructor and approval from us.  Please come to the project office hours to discuss with us if you would like to do a joint project.

In the proposal, below your project title, include the project category. The category can be one of:

  • Athletics & Sensing Devices
  • Audio & Music
  • Computer Vision
  • Finance & Commerce
  • General Machine Learning
  • Life Sciences
  • Natural Language
  • Physical Sciences
  • Theory & Reinforcement Learning
(If you feel a category is missing, please let us know.) To get a better idea of the different categories, check out this link: http://cs229.stanford.edu/proj2019spr/.

Project mentorsBased off of the topic you choose in your proposal, we’ll suggest a project mentor given the areas of expertise of the TAs. This is just a recommendation; feel free to speak with other TAs as well.

Your proposal should be a PDF document, giving the title of the project, the project category, the full names of all of your team members, the SUNet ID of your team members, and a 300-500 word description of what you plan to do.

Your project proposal should include the following information:
  • Motivation: What problem are you tackling? Is this an application or a theoretical result?
  • Method: What machine learning techniques are you planning to apply or improve upon?
  • Intended experiments: What experiments are you planning to run? How do you plan to evaluate your machine learning algorithm?
Presenting pointers to one relevant dataset and one example of prior research on the topic are a valuable (optional) addition.
GradingThe project proposal is mainly intended to make sure you decide on a project topic and get feedback from TAs early. As long as your proposal follows the instructions above and the project seems to have been thought out with a reasonable plan, you should do well on the proposal.


The milestone will help you make sure you're on track, and should describe what you've accomplished so far, and very briefly say what else you plan to do. You should write it as if it's an “early draft" of what will turn into your final project. You can write it as if you're writing the first few pages of your final project report, so that you can re-use most of the milestone text in your final report. Please write the milestone (and final report) keeping in mind that the intended audience are the instructors and the TAs. Thus, for example, you should not spend two pages explaining what logistic regression is. Your milestone should include the full names of all your team members and state the full title of your project. Note: We will expect your final writeup to be on the same topic as your milestone.

ContributionsPlease include a section that describes what each team member worked on and contributed to the project. This is to make sure team members are carrying a fair share of the work for projects. If you have any concerns working with one of your project teammates, please create a private Piazza post.
GradingThe milestone is mostly intended to get feedback from TAs to make sure you’re making reasonable progress. As long as your milestone follows the instructions above and you seem to have tested any assumptions which might prevent your team from completing the project, you should do well on the milestone.
Format Your milestone should be at most 3 pages, excluding references. Similar to to the proposal, it should include
  • Motivation: What problem are you tackling, and what's the setting you're considering?
  • Method: What machine learning techniques have you tried and why?
  • Preliminary experiments: Describe the experiments that you've run, the outcomes, and any error analysis that you've done. You should have tried at least one baseline.
  • Next steps: Given your preliminary results, what are the next steps that you're considering?

Poster Presentations

The class projects will be presented at a poster presentation. Each team should prepare a poster, and be prepared to give a very short explanation (3 minutes), in front of the poster, about their work. At the poster session, you'll also have an opportunity to see what everyone else did for their projects. We will supply poster-boards and easels for displaying the posters. You will also need to submit your poster as a PDF the day before the presentation.

FormatHere are some poster guidelines (please note that 36x24in means 36in wide by 24in tall, i.e. it's better if your poster is formatted landscape). You can also look at posters from previous years. Note: Despite example given in guidelines, posters with nice, illustrative figures are preferred over posters with lots of text.
GradingWe will be grading posters on the poster quality and clarity, the technical content of the poster, as well as the knowledge demonstrated by the team when discussing their work with teaching staff at the poster session.

There will be no late days for the poster submission.

Final Writeup

Because the teaching staff will have only a few hours to see a large number of posters at the poster session, we'll only be able to get an overview of the work you did at the session. We know that most students work very hard on the final projects, and so we are extremely careful to give each writeup ample attention, and read and try very hard to understand everything you describe in it.

After the class, we will also post all the final writeups online so that you can read about each other's work. If you do not want your write-up to be posted online, then please create a private Piazza post at least a week in advance of the final submission deadline.


Final project writeups can be at most 5 pages long (including appendices and figures). We will allow for extra pages containing only references. If you did this work in collaboration with someone else, or if someone else (such as another professor) had advised you on this work, your write-up must fully acknowledge their contributions. For shared projects, we also require that you submit the final report from the class you're sharing the project with.

Here's more detailed guidelines with a rough outline of what we expect to see in the final report: final-report-guidelines.pdf.
ContributionsPlease include a section that describes what each team member worked on and contributed to the project. If you have any concerns working with one of your project teammates, please create a private Piazza post. We may reach out and factor in contributions and evaluations when assigning project grades.
CodePlease include a link to a Github repository or zip file with the code for your final project. You do not have to include the data or additional libraries (so if you submit a zip file, it should not exceed 5MB).
GradingThe final report will be judged based off of the clarity of the report, the relevance of the project to topics taught in CS229, the novelty of the problem, and the technical quality and significance of the work.

There will be no late days for the final report.

After CS229

After CS229, if you want to submit your work to a machine learning conference, the ICML deadline will probably be in early February next year (http://icml.cc), and the NIPS deadline is usually in early June (http://nips.cc/). Of course, depending on the topic of your project, other non-machine learning conferences may also be more appropriate.

Project FAQs

1. What are the deliverables as part of the term project?
The project has four deliverables:
  1. Proposal
  2. Milestone
  3. Poster
  4. Final report

Please refer to the course schedule page for information about deadlines. We will post more details about each each on the website and on Piazza.

2. Should final project use only methods taught in classroom?

No, we don't restrict you to only use methods/topics/problems taught in class. That said, you can always consult a Project TA you are unsure about any method or problem statement.

3. Is it okay to use a dataset that is not public ?

We don't mind you using a dataset that is not public, as long as you have the required permissions to use it. We don't require you to share the dataset either as long as you can accurately describe it in the Final Report.

4. Is it okay to combine the CS229 term project with that of another class ?
In general it is possible to combine your project for CS229 and another class, but with the following caveats:
  1. You should make sure that you follow all the guidelines and requirements for the CS229 project (in addition to the requirements of the other class). So, if you'd like to combine your CS229 project with a class X but class X's policies don't allow for it, you cannot do it.
  2. You cannot turn in an identical project for both classes, but you can share common infrastructure/code base/datasets across the two classes.
  3. Clearly indicate in your milestone and final report, which part of the project is done for CS229 and which part is done for a class other than CS229. For shared projects, we also require that you submit the final report from the class you're sharing the project with.
5. Do all team members need to be enrolled in CS229?

No, but please explicitly state the work which was done by team members enrolled in CS229 in your milestone and final report. This extends to projects that were done in collaboration with research groups as well.

6. What are acceptable team sizes and how does grading differ as a function of the team size ?

We recommend teams of 3 students, while teams sizes of 1 or 2 are also acceptable. The team size will be taken under consideration when evaluating the scope of the project in breadth and depth, meaning that a three-person team is expected to accomplish more than a one-person team would.

The reason we encourage students to form teams of 3 is that, in our experience, this size usually fits best the expectations for the CS229 projects. In particular, we expect the team to submit a completed project (even for team of 1 or 2), so keep in mind that all projects require to spend a decent minimum effort towards gathering data, and setting up the infrastructure to reach some form of result. In a three-person team this can be shared much better, allowing the team to focus a lot more on the interesting stuff, e.g. results and discussion.

In exceptional cases, we can allow a team of 4 people. If you plan to work on a project in a team of 4, please come talk to one of the Project TAs beforehand so we can ensure that the project has a large enough scope.

7. Do I have to be on campus to submit the final report?

No, the final report will be submitted via Gradescope.

8. How do SCPD students submit the poster?

As an SCPD student you have the choice to either (1) attend the poster session or (2) submit on Gradescope the poster PDF and a link to a 3-minute recording of your presentation (done as if in person).

9. Is it okay for non-SCPD students to miss the poster session?
Part of your project grade part depends on your presentation at the poster session, so we really urge you not to miss it. That said, if (and only if) you have a final exam conflict there are a few possibilities:
  1. If your other class offers an alternative time for the exam, you should choose that.
  2. If you are working on the project as a team, the rest of the team could present the poster without you there. As long as your project is represented by a subset of the team, that should be sufficient.
  3. If none of above options work for you, come talk to one of the Project TAs or post a message on Piazza.
10. What fraction of the final grade is the project?

The term project is 40% of the final grade.

11. What is the late day policy for group project?

Each of the team members must use one late day if they wish to extend the deadline by a day. Late days cannot be used for the final project poster or write-up.

12: does the team have to be all SCPD students or all on-campus students?

A team can have both on-campus and SCPD students.

13: can we use some Machine Learning libraries such as scikit-learn or are we expected to implement them from scratch?

You can use any library for the project.

14: Is it ok to use a public repository for version control?

A private repository is recommended (and free with GitHub's Education Pack), but a public repository is also okay.

15: What if two teams end up working on the same project?

It is okay if two teams end up working on the same project as long as they don’t coordinate to do so, in order to not be biased in the way they tackle the problem. Alternatively the teams can coordinate to make sure they work on different problems.

16: Will we be provided any cloud compute resource credit?

We are looking into getting cloud credit for the projects. We will announce on Piazza once this is finalized. Also check out Google Colab for free GPU resources.

17: Are we required to use Python for the project?

Any programming language is allowed for the project.