In the final project, you will work in groups of up to four to apply the techniques that you've learned in CS221 to a new setting that you're interested in. Note that regardless of the group size, all groups must submit the work detailed in each milestone and will be graded on the same criteria. Additionally, we expect each team to submit a completed project. We encourage teams of 3-4 students. All projects require students to spend time gathering data and setting up the infrastructure to reach an end result, and thus 3 or 4 person teams can share these tasks much better. This allows the team to focus more on the interesting results and discussion in the project. Each member of the team should contribute in both technical and non-technical components of the project.

You will build a system to solve a well-defined task. Which task you choose is completely open-ended, but the methods you use should draw on the ones from the course.

If you are interested in doing the class project, submit the project interest form any time before Oct 7. We encourage all students who might be interested in a project to fill this form out. There is no commitment (you can fill it out and then not follow through with doing a project). You can also refer to this Ed Post to find project group team members.

After you submit the form, you will be assigned one of the CAs as your official mentor. They will grade all your work and really get to know your project. You are required to have a 15-minute check-in meeting with your mentor in the week before or after the progress report deadline, and are encouraged to drop by your mentor's office hours often to discuss your project. Note that it will take several iterations to find the right project, so be patient; this exploration is an essential part of research, so learn from it. Have fun and don't wait until the last minute!

Milestones

Throughout the quarter, there will be several milestones so that you can get adequate feedback on the project. While the project is ungraded (except for potential extra credit), we can only give feedback on milestones that are submitted on time.

1. Proposal (2 pages max). The proposal is due Oct 21. It should include the following items.
   • Problem Statement and Task Definition: What does your system do (what is its input and output)? What real-world problem does this system try to solve? Make sure that the scope of the project is not too narrow or broad. For example, building a system to answer any natural language question is too broad, whereas answering short factoid questions about movies is more reasonable.
   • Input/Output Behavior with concrete examples of both the inputs and outputs (explain what the input and output variables should look like and how they interact with the system). You should collect some preliminary data that you can use in your description of the input and output behavior. Specifically reference what data you are using.
   • An evaluation metric. How will you measure the success of your system? Why does this metric work best for this problem? For this, you need to obtain a reasonably sized dataset of example input-output pairs, either from existing sources, or collecting one from scratch. A natural evaluation metric is accuracy, but it could be memory or running time.
   • Related works. Search the Internet for similar projects and mention the related research and projects.
   • Baseline and Oracle. Before developing your primary approach, you should implement baselines and oracles (See more about these in the FAQ at the bottom of the page). These are really important as they give you intuition for how easy or hard the problem you're solving is. Intuitively, baselines give lower bounds on the performance you will obtain and oracles give upper bounds. If this gap is too small, then you probably don't have a good task. Importantly, baselines and oracles should be relatively easy to implement and can be done before you invest a lot of time in implementing a fancier approach. In this part of the proposal, you should describe what your baseline and oracle are. While you do not need to implement it for the proposal, it is recommended that you have started implementing them by this point.
   • Methodology How will you approach the problem? Why does this method fit with your problem? Identify the challenges of building the system and the phenomena in the data that you're trying to capture. How should you model the task (e.g., using search, machine learning, logic, etc.)? There will be many ways to do this, but you should pick one or two and determine how the methods address the challenges as well as any pros and cons. What algorithms are appropriate for handling the models that you came up with, and what are the tradeoffs between accuracy and efficiency? Are there any implementation choices specific to your problem?
   • Description of the challenges. What are the challenges? Which topics (e.g., search, MDPs, etc.) might be able to address those challenges (at a high-level, since we haven't covered any techniques in detail at this point)?
   
   You should have the majority of the infrastructure (e.g., building a simulator, cleaning data) completed to do something interesting by now. For machine learning tasks, setting up the infrastructure involves collecting data (either by scraping, using crowdsourcing, or hand labeling). For game-based tasks, this involves building the game engine/simulator. While infrastructure is necessary, try not to spend too much time on it. You can sometimes take existing datasets or modify existing simulators to save time, but if you want to solve a task you care about, this is not always an option. Note that if you download existing datasets which are already preprocessed (e.g., Kaggle), then you will be expected to do more with the project.
   
   Note that you can still adjust your project topic after submitting the proposal, but your progress report (described next) should be on the same topic as your final report.
   
   
2. Progress report (4 pages max). The progress report is due on Nov 14. It should include the following items.
   • Introduction - Brief overview of your problem.
   • Literature Review - Description of other work/papers you've found that are related to your task. Just mentioning a paper is not sufficient; you should at least go into brief detail about what kind of approach they are using/how it relates to your work if it's not immediately clear. When looking at relevant literature examine if there have there been other attempts to build a similar system. Compare and contrast your approach with existing work, citing the relevant papers. The comparison should be more than just high-level descriptions. You should try to fit your work and other work into the same framework. Are the two approaches complementary, orthogonal, or contradictory?
   • Dataset - Description of data you are using - size of dataset, distribution of classes, any preprocessing you needed to do.
   • Baseline - Description and implementation of your baseline. Please provide a detailed description of your implemented baseline along with evaluation of the baseline using the metrics you define.
   • Main approach - Propose a model and an algorithm for tackling your task. You should describe the model and algorithm in detail and use a concrete example to demonstrate how the model and algorithm work. Don't describe methods in general; describe precisely how they apply to your problem (what are the inputs/outputs, variables, factors, states, etc.)?
   • Evaluation Metric - Please include what metrics, both qualitative and quantitative, you are using to evaluate the success of your problem. If relevant please include equations to describe your metrics.
   • Results & Analysis - At this point, you should have fully implemented your baseline and also have a basic working implementation of your main approach. Please include the performance of your baseline as well as the performance of your main approach so far. Include an analysis of your results, and how this might inform your next steps in fine-tuning your main approach.
   • Future Work - This is not mandatory, but it might be helpful for your mentor to get an idea of what next steps you plan to take after the milestone.
   • References - Please include a reference section with properly formatted citations. (Not included in 4 page limit).


3. Progress check-in with CA mentor (15-minute meeting). The check-in should be completed by Nov 14. To provide more mentorship for the final project, we introduce a progress check-in between project groups and their assigned mentor. In the week before or after the progress report deadline, you will have a 15-minute meeting with your mentor to discuss the progress of your project, clear any confusion, and get advice. Your assigned CA mentor will reach out to give information on how to schedule the meeting for check-in.


4. Final Video (5 minutes): The final video is due on Dec 3. The final video is a 5 minute video presentation of your project. In the video, you should briefly describe the motivation, problem definition, challenges, approaches, results, and analysis. You should include diagrams, figures and charts to illustrate the highlights of your work. If possible, try to come up with creative visualizations of your project. These could include system diagrams, more detailed examples of data that do not fit in the space of your report, or live demonstrations for end-to-end systems. The goal of the video is to convey the important high-level ideas and give intuition rather than be a super-detailed specification of everything you did. Use lots of diagrams and concrete examples, and avoid slides that are too wordy or have extremely complex equations.


5. Final Report (5-10 pages max): The final report is due on Dec 3. Your final report should be a comprehensive account of your project. This final report structure is very similar to the progress report, except we would like to see some new results, experimentation and/or analysis, and a Future Works section. Below is a full description of what you should include in your project final report.
   • Introduction - Brief overview of your problem. Why might this problem be important?
   • Literature Review - Description of other work/papers you've found that are related to your task. Just mentioning a paper is not sufficient; you should at least go into brief detail about what kind of approach they are using/how it relates to your work if it's not immediately clear. Please also mention why your work relates or differs from these related works.
   • Dataset - Description of data you are using - size of dataset, distribution of classes, any preprocessing you needed to do
   • Baseline - Description and implementation of your baseline. For this report, you don't need to go too much into detail, but please still include some details.
   • Main Approach - Propose a model and an algorithm for tackling your task. You should describe the model and algorithm in detail and use a concrete example to demonstrate how the model and algorithm work. Don't describe methods in general; describe precisely how they apply to your problem (what are the inputs/outputs, variables, factors, states, etc.)?
   • Evaluation Metric - Please include what metrics, both qualitative and quantitative, you are using to evaluate the success of your problem. If relevant please include equations to describe your metrics.
   • Results & Analysis - At this point, you should have expanded on your approach from the progress report. Please include the performance of your baseline as well as the performance of your main approach so far and any experiments that you have run. Also, include an analysis of your results, and how this might inform your next steps in fine-tuning your main approach. The analysis is very important, and it requires you to think about what your results might mean.
   • Error Analysis - Describe a few experiments that you ran that show the properties (both pros and cons) of your system. Analyze the data and show either graphs or tables to illustrate your point.What's the take-away message? Were there any surprises? Use these experiments in the error analysis to describe potential errors in the method and why they may have occurred.
   • Future Work - We are requiring this section this time. This section can be short, but please include some ideas about how you could improve your model if you had more time. This can also include any challenges you're running into and how you might fix it.
   • Ethical Considerations - Provide a 1-2 paragraph statement outlining at least one ethical issue or societal risk specific to your project, with an explanation of what in particular connects your project to the ethical issue(s) or societal risk(s) raised. Subsequently, you also need to explain at least 1 possible mitigation strategy for each of those issues (e.g. technical modifications, policy changes, or specific model deployment measures). Note that you are not required to implement these mitigation strategies in your final project.
   • Code - Please include a link to your Github/Bitbucket/etc. For private repos, make sure to communicate this with your mentor and get their Github ID to add to your repo. If you choose to upload a zip, you can choose a subset of the data to upload so that its size won't be too large.
   • References - Please include a reference section with properly formatted citations (any format of your choice).

Note: you can have an appendix for each of the assignments the beyond the maximum number of allowed pages with any figures, plots, or examples that you need. References do not count for the page limit.

Submit the milestones on Gradescope and make sure all group members are added to the submission.

All milestones are due at 11:59pm.

• proposal.pdf, progress.pdf, or final.pdf containing a PDF of your writeup.

Rubric

We will give feedback on the following dimensions:

• Task definition: is the task precisely defined and is the motivation for the task clear? In other words, does the world somehow become a better place if your project were successful? We will reward projects that are extra thoughtful about how to use AI for social impact.
• Approach: was a baseline, an oracle, and an advanced method described clearly, well justified, and tested?
• Data and experiments: have you explained the data clearly, performed systematic experiments, and reported concrete results?
• Analysis: did you interpret the results and try to explain why things worked (or didn't work) the way they did? Do you show concrete examples?

Of course, the experiments may not always be successful. Getting negative results is normal, and as long as you make a reasonably well-motivated attempt and you explained why the results came out negative, you will get credit.

This is a suggestion of how to approach the final project with an example.

• Pick a topic that you're passionate about (e.g., food, language, energy, politics, sports, card games, robotics). As a running example, say we're interested in how people read the news to get their information.

• Brainstorm to find some tasks on that topic: ask "wouldn't it be nice to have a system that does X?" or "wouldn't it be nice to understand X?" A good task should not be too easy (sorting a list of numbers) and not too hard (building a system that can automatically solve CS221 homeworks). Please come to office hours for feedback on finding the right balance. Let's focus on recommending news to people.

• Define the task you're trying to solve clearly and convince yourself (and a few friends) that it's important/interesting. Also state your evaluation metric – how will you know if you have succeeded or not? Concentrate on a small set of popular news sites: nytimes.com, slashdot.org, sfgate.com, onion.com, etc. For each user and each day, assume we have acquired a set of articles that the user is interested in reading (training data). Our task is to predict for a new day, given the full set of articles, the best subset to show the user; evaluation metric would be prediction accuracy.

• Gather and clean the necessary data (this might involve scraping websites, filtering outliers, etc.). This step can often take an annoyingly large amount of time if you're not careful, so do not try to get bogged down here. Simplify the task or focus on a subset of the data if necessary. You might find yourself adjusting the task you're trying to solve based on new empirical insights you get by looking at the data. Notice that even if you're not doing machine learning, it's necessary to have data for evaluation purposes. Write some scripts that download the RSS feeds from the news sites, run some basic NLP processing (e.g., tokenization), say, using NLTK.

• Implement a baseline algorithm. For a classification task, this would be always predicting the most common label. If your baseline is too high, then your task is probably too easy. One baseline is to always produce the first document from each news site. Also implement an oracle, for example, recommending the document based on the number of comments. This is an oracle because you wouldn't have the number of comments at the time you actually wanted to recommend the article!

• Formulate a model and implement the algorithm for that model. You should try several variants and compare them. Remember to try as much as possible to separate model (what you want to compute) from algorithms (how you do it). You might train a classifier to predict, for each news article, whether to include it or not. You might try to include these predictions as factors in a weighted CSP and try to find a set of articles that balance diversity and relevance.

• Perhaps the most important part of the project is the final step, which is to analyze the results. It's more important that you do a thorough analysis and interpret your results rather than implement a huge number of complicated heuristics in trying to eke out the maximum performance. The analysis should begin with basic facts, e.g., how much time/memory did the algorithm take, how does the accuracy vary with the amount of training data? What are the instances that your system does the worst on? Give concrete examples and try to understand why. Is there a bottleneck? Is it due to lack of training data?

You are free to use existing datasets, but these might be not necessarily the best match for your problem, in which case you are probably better off making your own dataset.

• Kaggle is a website that runs machine learning competitions for predicting for monetary reward.
• Past CS229 projects: examples of machine learning projects that you can look at for inspiration. Of course your project doesn't have to use machine learning – it can draw from other areas of AI.
• SAT competition: satisfiability problems are a special important class of CSPs.
• Natural language processing datasets: links to many NLP datasets for different languages.
• OpenAI Gym: environments for reinforcement learning related projects.

Libraries

You are free to use existing tools for parts of your project as long as you're clear what you used. When you use existing tools, the expectation is that you will do more on other dimensions.

• scikit-learn: machine learning library implemented in Python
• Natural language Toolkit (NLTK): a set of tools for basic NLP in Python
• OpenCV: Python libraries for simple computer vision

Some project ideas

• Predict the price of airline ticket prices given day, time, location, etc.
• Predict the amount of electricity consumed over the course of a day.
• Predict whether the phone should be switched off / silenced based on sensor readings from your smartphone.
• Auto-complete code when you're programming.
• Answer natural language questions for a restricted domain (e.g., movies, sports).
• Search for a mathematical theorem based on an expression which normalizes over variable names.
• Find the optimal way to get from one place on Stanford campus to another place, taking into account uncertain travel times due to traffic.
• Solve Sudoku puzzles or crossword puzzles.
• Build an engine to play Go, chess, 2048, poker, etc.
• Break substitution codes based on knowledge of English.
• Automatically generate the harmonization of a melody.
• Generate poetry on a given topic.

You can also get inspiration from previous years' CS221 projects (student access only).

Frequently asked questions

Can I use the same project for CS221 and another class (CS229, etc.)? The short answer is that you cannot turn in the identical project for both classes, but you can share common infrastructure across the two classes. First, you should make sure that you follow the guidelines for the CS221 project, which are likely different from those of other classes. Second, if any part of the project is done for a purpose outside CS221 (for the final project in CS229 or other classes, or even for your own research), then in the progress and final reports, you must clearly indicate which part of the project was done for CS221 and which part was not. For example, if you're taking CS229, then you cannot turn in the same pure machine learning project for CS221. But you can work on the same broad problem (e.g., news recommendation) for both classes and share the same dataset / generic wrapper code. You should then explore the machine learning aspect of the problem for CS229 (e.g., classifying news relevance) and another topic for CS221 (e.g., optimizing diversity across news articles using search or CSPs).

Are there restrictions on who I can partner up with for the final project? The only hard requirement is that each member of your group must be enrolled in CS221. Thus, if you choose to use the same project for CS221 and another class, all of your partners must be in CS221. If you feel like you have a compelling case for an exception, please submit a request on Ed detailing the parts of the project used for each class and the reasons for deviating from the project policies.

How do you choose a good baseline and oracle?
Baselines are simple algorithms, which might include using a small set of hand-crafted rules, training a simple classifier, etc. (Note that baselines are extremely simple, but you might be surprised at how effective they are.) While a predictor that guesses randomly provides a lower bound (and can be reported in the paper), it is too simple and doesn't give you much information. Predicting the majority label is a slightly less trivial baseline, and whether it's acceptable depends on how insightful it is. For classification, if the different labels have very different proportions, then it could be useful; otherwise it won't be. You are encouraged to have multiple baselines. Please note that we expect an implementation of the baseline for the project progress report (not project proposal).

Oracles are algorithms that "cheat" and look at the correct answer or involve humans. For human-like classification problems (e.g., sentiment classification), you can have each member of your project team try to annotate ~50 examples and measuring the agreement rate. Note that some tasks are subjective, so even though humans are providing ground truth labels, human accuracy will not be 100%. When the classification problem is not human-like, you can try to use the training error of an expressive classifier (e.g., nearest neighbors) as a proxy for oracle error. The idea is that if you can't even fit the training data using a very expressive classifier, there is probably a lot of noise in your dataset, and you have a slim chance of building any classifier that does well on test. While returning 100% is an upper bound, it is not a valid oracle since it is vacuously an upper bound. Sometimes, oracles might be difficult to come by. If you think that no good oracles exist, explain why. Please note that we do not expect an implementation of the oracle at any point during your final project. However if the implementation is very easy, you are free to implement the oracle.

Both baselines and oracles should be simple and not take much time. The point is not to do something fancy, but to work with the data / problem that you have in a substantive way and learn something from it. Here are some examples of baselines:

• Manually hand-code a few simple rules
• Train a classifier with some simple features

• Manually have each member of your team hand label a handful of examples. The oracle is the agreement rate between people.
• Measure the training accuracy using reasonable features. The idea is that if you can't even fit your training data very well, then your test error is probably going to be even worse.
• Predict the label given information that you wouldn't normally have a test time (for example predicting the future given other information from the future).