CS221: Artificial Intelligence: Principles and Techniques

Communication: We will use Ed for all communications. We encourage all students to use Ed, either through public or private posts. However, if you have an issue that you would like to discuss privately, you can also email us at cs221-aut2022-staff@lists.stanford.edu, which is read by only the faculty and the co-head CAs.

Academic accommodations: If you need an academic accommodation, 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. It is the student's responsibility to reach out to the teaching staff regarding the OAE letter. Please send your letters to cs221-aut2022-staff@lists.stanford.edu by Friday, October 8 (week 3).

Course structure: To ensure accessibility, CS221 will be offered as a remote course in Autumn 2021. Below is an overview of the course components:

Modules (videos and slides): All lecture materials will be delivered through modules, pre-recorded course videos that students can watch at their own time. Each week's modules are listed in the schedule and can be accessed here.
Faculty chats: In hopes of encouraging you to dive deeper into an AI-related topic that is of particular interest to you, during our class time on Mondays, we will have small-group discussions between students and faculty about current topics in AI. Each of these is a 25-minute session with a designated topic. Throughout the quarter, each student will be assigned to one session based on their topic preferences. Note: Since this is a synchronous component and attendance to your assigned session is mandatory, please make sure that you are available during class time on Mondays to attend the faculty chat.
Problem sessions: During our class time on Wednesdays, we will hold problem sessions where CAs will guide students to work together through practice problems.
Remote HW OH/Homework parties: We will hold weekly remote HW OH/homework parties for students to get together and work on homework. CAs will be present to answer any questions. We encourage you to come for these over 1:1 office hours whenever possible, since your fellow classmates might benefit from hearing your questions as well.
Office hours with faculty and CAs: Our CAs will hold office hours in two different formats:
- Virtual 1:1 OH (most common): These will be appointment-based so that students need not wait in the queue.
- In-person open OH (limited availability): These are open to all students and require no appointment.
There are two different types of office hours with CAs:
- Homework OH, specifically for questions about the homework
- General OH (i.e., non-homework OH), for all other questions (including about modules, quizzes, and projects)
Our faculty will hold virtual 1:1 OH.
See our calendar for OH times as well as sign-up links for 1:1 OH. We will make sure that there are enough office hours for students each week. Please contact us through Ed if you want to schedule one but cannot find an available slot.

See coursework for details on deliverables for the class.

Platforms: Besides Ed, we will use a combination of Zoom and Nooks to hold class activities and remote office hours. All homework, exam, and project submissions will be handled through Gradescope, where you will also find your grades. We will send out links and access codes to enrolled students through Canvas.

Time/Location: All class activities and office hours are in our class calendar. All following times are in Pacific Time (PT):

Modules: Each week's modules will be posted in our schedule on the Friday of the previous week.
Faculty chats (Zoom): a 25-minute block between 1:30-3:00pm on a Monday (class time); see above for more details.
Problem Sessions (Zoom): Wednesdays 1:30-3:00pm (class time).
Remote HW OH/Homework Parties (Nooks): Thursdays 2-4pm, Saturdays 10am-12pm, Sundays 7-9pm.
Office Hours (Nooks or Huang): Throughout the week; sign-up links are on our class calendar. If you can no longer make it to your slot, please free it up as soon as possible so that other students can sign up.

Instructors:

Percy Liang
(office hours)

Dorsa Sadigh
(office hours)

Course Assistants:

Shiori Sagawa
(office hours)
Co-Head CA

Fei Fang
(General OH)
Co-Head CA

Lauren Gillespie
(Homework OH)

Samar Khanna
(General OH)

Tina Li
(Homework OH)

Madeline Liao
(Homework OH)

Sam Lowe
(General OH)

Amrita Palaparthi
(Homework OH)

Michael Piseno
(Homework OH)

Kendrick Shen
(Homework OH)

Arvind Sridhar
(Homework OH)

Yuchen Wang
(General OH)

Yilin Wu
(Homework OH)

Kathy Yu
(Homework OH)

Zhengping Zhou
(General OH)

What is this course about? What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of artificial intelligence (AI) is to tackle these with rigorous mathematical tools. In this course, you will learn the foundational principles that drive these applications and practice implementing some of these systems. Specific topics include machine learning, search, game playing, Markov decision processes, constraint satisfaction, graphical models, and logic. The main goal of the course is to equip you with the tools to tackle new AI problems you might encounter in life.

Prerequisites: This course is fast-paced and covers a lot of ground, so it is important that you have a solid foundation on both the theoretical and empirical fronts. You should have taken the following classes (or their equivalents):

Programming (CS 106A, CS 106B, CS 107)
Discrete math (CS 103)
Probability (CS 109)
Linear algebra (Math 51)

Reading: There is no required textbook for this class, and you should be able to learn everything from the lecture notes and homeworks. However, if you would like to pursue more advanced topics or get another perspective on the same material, here are some books:

Russell and Norvig. Artificial Intelligence: A Modern Approach. A comprehensive reference for all the AI topics that we will cover.
Koller and Friedman. Probabilistic Graphical Models. Covers factor graphs and Bayesian networks (this is the textbook for CS228).
Sutton and Barto. Reinforcement Learning: An Introduction. Covers Markov decision processes and reinforcement learning. Available free online.
Hastie, Tibshirani, and Friedman. The elements of statistical learning. Covers machine learning. Available free online.
Tsang. Foundations of constraint satisfaction. Covers constraint satisfaction problems. Available free online.

Bear in mind that some of these books can be quite dense and use different notation terminology, so it might take some effort to connect up with the material from class.

Video access disclaimer: A portion of class activities will be given and recorded in Zoom. For your convenience, you can access these recordings by logging into the course Canvas site. These recordings might be reused in other Stanford courses, viewed by other Stanford students, faculty, or staff, or used for other education and research purposes. If you have questions, please contact a member of the teaching team.

Grading

Homeworks (55%): There will be weekly homeworks with both written and programming parts. Each homework is centered around an application and will also deepen your understanding of the theoretical concepts. Some homeworks will have a competition component; winners will receive extra credit. Here are all the homework deadlines:

Exams (40%): There will be two open-book non-coding exams that will test your knowledge and problem-solving skills on preceding lectures and homeworks.
Exam 1: released at 3:15PT on Friday, Oct 29; due at 3:15PT on Saturday, Oct 30
Exam 2: released at 3:15PT on Wednesday, Dec 8; due at 3:15PT on Thursday, Dec 9

Length: 100 minutes each.

Format: The exam will be distributed and administered through Gradescope and will be available for 24 hours. Students can choose any 100-minute block of time within the designated window above to take the exam.

Conflict: If you have a major time conflict for either exam, you should let us know privately via email by Friday, October 8 (week 3).

Faculty chat participation (5%): Each discussion topic is associated with a set of discussion materials. You are expected to:
- engage with the materials beforehand,
- show up on time to your assigned session, and
- at the beginning of the session, introduce yourself and share your personal goal for the session (e.g., what particular question you'd like to discuss, what you hope to get out of the session).
Full credit is granted for fulfillment of these expectations. You are also encouraged but not required to keep your camera on.

Project (optional, ungraded): The final project provides an opportunity for you to use the tools from class to build something interesting of your choice. Projects should be done in groups of one to four students. Each project group will be assigned a CA mentor who will give feedback and answer questions, and thus the project will be an opportunity for mentorship. We will award up to 3% of extra credit to projects that are done well.
The project will be something that you work on throughout the course and we have set up some milestones to help you along the way:
- Project interest form [p-interest] (due Tue Oct 5)
- Project proposal [p-proposal] (due Tue Oct 19)
- Project progress report [p-progress] (due Tue Nov 9)
- Project final report and video [p-final] (due Tue Nov 30)
See the project page for more details.

Regardless of the group size, all groups must submit the work detailed in each milestone and will be graded on the same criteria. Although we allow 1-2 person project groups, we encourage groups of 3-4 members. We encourage teams of 3-4 students because this size typically best fits the expectations for CS 221 projects. We expect each team to submit a completed project (even for team of 1 or 2). All projects require that students spend time gathering data, and setting up the infrastructure to reach an end result. A 3 or 4 person team can share these tasks much better, allowing 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. We will provide resources on Ed and the project page that can help you find group members.

For inspiration, we have made some previous CS221 projects available for viewing.
Ed: You will be awarded with up to 2% extra credit if you answer other students' questions in a substantial and helpful way.

Homeworks

Written assignments: Homeworks should be written up clearly and succinctly; you may lose points if your answers are unclear or unnecessarily complicated. Here is an example of what we are looking for. Written homework must be typeset using LaTeX, Microsoft Word, Pages for Mac, or an equivalent program, and submitted as a PDF. We strongly encourage you to use LaTeX: there are user-friendly web interfaces like Overleaf. See this getting started guide. Figures may be hand-drawn, so long as they are included in a typeset PDF. If you're not sure where to start with formatting your written solutions, we recommend using this template as a starting point. You can copy and paste its contents into an Overleaf project or upload it directly.

Programming assignments: The grader runs on Python 3.7, which is not guaranteed to work with alternative versions (e.g., Python 2.7). Please use Python 3.7 to develop your code.

The programming assignments are designed to be run in GNU/Linux environments. Most or all of the grading code may incidentally work on other systems such as MacOS or Windows, and students may optionally choose to do most of their development in one of these alternative environments. However, no technical support will be provided for issues that only arise on an alternative environment. Moreover, no matter what environment is used during development, students must confirm that their code (specifically, the student's submission.py) runs on Gradescope.

The submitted code will not be graded if it has one of the following issues:

The original grader.py script (operating on the submitted submission.py) may not exit normally if you use calls such as quit(), exit(), sys.exit(), and os._exit(). Also note that Python packages outside the standard library are not guaranteed to work. Therefore, do not use packages like numpy, scikit-learn, and pandas.
The code reads external resources other than the files given in the assignment.
The code is malicious. This is considered a violation of the honor code. The score of the assignment will be zero (0) and the incident will be reported to the Office of Judicial Affairs.

Collaboration policy and honor code: You are free to form study groups and discuss homeworks and projects. However, you must write up homeworks and code from scratch independently, and you must acknowledge in your submission all the students you discussed with. The following are considered to be honor code violations:

Looking at the writeup or code of another student.
Showing your writeup or code to another student.
Discussing homework problems in such detail that your solution (writeup or code) is almost identical to another student's answer.
Uploading your writeup or code to a public repository (e.g. github, bitbucket, pastebin) so that it can be accessed by other students.
Looking at solutions from previous years' homeworks - either official or written up by another student.

When debugging code together, you are only allowed to look at the input-output behavior of each other's programs (so you should write good test cases!). It is important to remember that even if you didn't copy but just gave another student your solution, you are still violating the honor code, so please be careful. We periodically run similarity-detection software over all submitted student programs, including programs from past quarters and any solutions found online on public websites. Anyone violating the honor code will be referred to the Office of Judicial Affairs. If you feel like you made a mistake (it can happen, especially under time pressure!), please reach out to the instructor or the head CA; the consequences will be much less severe than if we approach you.

Submission

Electronic Submission: All homework assignments are due at 11pm (23:00, not 23:59) Pacific time on the due date.
Refer to coursework for exam deadlines. Assignments are submitted through Gradescope. Do not submit your assignment via email. If anything goes wrong, please ask a question on Ed or contact a course assistant. If you need to sign up for a Gradescope account, please use your @stanford.edu email address. You can submit as many times as you'd like until the deadline: we will only grade the last submission. Submit early to make sure your submission runs properly on the Gradescope servers. Gradescope will run grader.py on the programming questions and give you feedback on non-hidden test cases. You are responsible for checking that your program runs properly on these cases. You will not get credit otherwise. If anything goes wrong, please ask a question on Ed or contact a course assistant. Do not email us your submission. Partial work is better than not submitting any work.

For assignments with a programming component, we will automatically sanity check your code in some basic test cases, but we will grade your code on additional test cases. Important: just because you pass the basic test cases, you are by no means guaranteed to get full credit on the other, hidden test cases, so you should test the program more thoroughly yourself!

Unless the assignment instructs otherwise, all of your code modifications should be in submission.py and all of your written answers in <assignment ID>.pdf. Upload the former to Gradescope under the "Programming" section, and the latter under the "Written" section.

For the project milestones, make sure that all members of your group submit. The submission should include a group.txt file which should contain the SUNetIDs of the entire group, one per line.

Late days: A homework is $\lceil d \rceil$ days late if it is turned in $d$ days past the due date (note that this means if you are $1$ second late, $\lceil d \rceil = 1$ and it is 1 day late). You have seven (7) late days in total that can be distributed among the homeworks without penalty. After that, the maximum possible grade is decreased by 25% each day (so the best you can do with $d = 1$ is 75%). As an example, if you are out of late days and submit one day late, a 90 will be capped at a 75, but a 72 will not be changed. Note that we will only allow a max of $d = 2$ late days per homework, though, so if $d > 2$ then we will not accept your submission. Gradescope is set up to accept written and programming submissions separately. Late days are calculated per-assignment, with the number of late days used depending on the later submission. So, if the programming part is $1$ day late and the written part is $2$ days late, then that will count as using $2$ late days.

No late days can be taken for exams; you can start the timed exams any time during the one-day window.

Regrades: If you believe that the course staff made an objective error in grading, then you may submit a regrade request for the written part of your homework or exam. Remember that even if the grading seems harsh to you, the same rubric was used for everyone for fairness, so this is not sufficient justification for a regrade. It is also helpful to cross-check your answer against the released solutions. If you still choose to submit a regrade request, click the corresponding question on Gradescope, then click the "Request Regrade" button at the bottom. Any requests submitted over email or in person will be ignored. Regrade requests for a particular assignment are due by Monday 4pm, one week after the grades are returned. Note that we may regrade your entire submission, so that depending on your submission you may actually lose more points than you gain.

Nooks links included in the schedule below assume you have already signed into our Nooks space following our class specific link shared on Canvas and Ed.

	Monday	Tuesday	Wednesday	Thursday	Friday	Saturday	Sunday
Week 1	Sep 20	Sep 21	Sep 22	Sep 23	Sep 24	Sep 25	Sep 26
Modules Introduction Prerequisites Machine Learning I Homework [Foundations] Survey [Faculty chat assignment]	Live Lecture: Introduction (Dorsa) 1:30-3:00pm		Problem Session 1 1:30-3:00pm [Problems] [Solutions] [Video] Faculty chat assignment survey due	HW Party 2-4pm	HW Party 4-6pm	HW Party 10am-12pm
Week 2	Sep 27	Sep 28	Sep 29	Sep 30	Oct 1	Oct 2	Oct 3
Modules Machine Learning II Homework [Sentiment]	Faculty chats 1:30-3:00pm Foundations HW due		Problem Session 2 1:30-3:00pm [Problems] [Solutions] [Video]	HW Party Changed: 3-5pm	HW Party 4-6pm	HW Party 10am-12pm
Week 3	Oct 4	Oct 5	Oct 6	Oct 7	Oct 8	Oct 9	Oct 10
Modules Search Homework [Reconstruct]	Faculty chats 1:30-3:00pm Sentiment HW due	Project interest form due	Problem Session 3 1:30-3:00pm [Slides] [Problems] [Solutions] [Video]	HW Party 2-4pm	HW Party 4-6pm Final Study List Deadline	HW Party 10am-12pm
Week 4	Oct 11	Oct 12	Oct 13	Oct 14	Oct 15	Oct 16	Oct 17
Modules MDPs I Homework [Blackjack]	Faculty chats 1:30-3:00pm Reconstruct HW due		Problem Session 4 1:30-3:00pm [Slides] [Problems] [Solutions] [Video]	Remote HW OH (drop-in) Changed: 3:15-5:15pm		Remote HW OH (drop-in) 10am-12pm	Remote HW OH (drop-in) 7-9pm
Week 5	Oct 18	Oct 19	Oct 20	Oct 21	Oct 22	Oct 23	Oct 24
Modules MDPs II Games Homework [Pacman]	Faculty chats 1:30-3:00pm Blackjack HW due	Project proposal due	Problem Session 5 1:30-3:00pm [Slides] [Problems] [Solutions] [Video]	Remote HW OH (drop-in) 2-4pm		Remote HW OH (drop-in) 10am-12pm	Remote HW OH (drop-in) 7-9pm
Week 6	Oct 25	Oct 26	Oct 27	Oct 28	Oct 29	Oct 30	Oct 31
Modules N/A	Faculty chats 1:30-3:00pm Pacman HW due		Problem Session 6 1:30-3:00pm [Slides] [Video]		Exam 1 released at 3:15pm PT	Exam 1 due at 3:15pm PT
Week 7	Nov 1	Nov 2	Nov 3	Nov 4	Nov 5	Nov 6	Nov 7
Modules Factor Graphs Homework [Scheduling]	Faculty chats 1:30-3:00pm		Problem Session 7 1:30-3:00pm [Slides] [Problems] [Solutions] [Video]	Remote HW OH (drop-in) Changed: 3:15-5:15pm		Remote HW OH (drop-in) 10am-12pm	Remote HW OH (drop-in) 7-9pm
Week 8	Nov 8	Nov 9	Nov 10	Nov 11	Nov 12	Nov 13	Nov 14
Modules Bayesian Networks Homework [Car]	Faculty chats 1:30-3:00pm Scheduling HW due	Project progress report due	Problem Session 8 1:30-3:00pm [Slides] [Problems] [Solutions] [Video]	Remote HW OH (drop-in) 2-4pm		Remote HW OH (drop-in) 10am-12pm	Remote HW OH (drop-in) 7-9pm
Week 9	Nov 15	Nov 16	Nov 17	Nov 18	Nov 19	Nov 20	Nov 21
Modules Logic Homework [Logic]	Faculty chats 1:30-3:00pm Car HW due		Problem Session 9 1:30-3:00pm [Slides] [Problems] [Solutions] [Video]	Remote HW OH (drop-in) Changed: Moved to Friday	Remote HW OH (drop-in) Changed: 5:00-7:00pm
Thanksgiving	Nov 22	Nov 23	Nov 24	Nov 25	Nov 26	Nov 27	Nov 28
Break week!						Remote HW OH (drop-in) 10am-12pm	Remote HW OH (drop-in) 7-9pm
Week 10	Nov 29	Nov 30	Dec 1	Dec 2	Dec 3	Dec 4	Dec 5
Modules Conclusion	Live Lecture: Conclusion (Percy) 1:30-3:00pm [Video] Logic HW due	Project final report due	Problem Session 10 1:30-3:00pm [Slides] [Video]
Finals Week	Dec 6	Dec 7	Dec 8	Dec 9	Dec 10	Dec 11	Dec 12
			Exam 2 released at 3:15pm PT	Exam 2 due at 3:15pm PT