CS221: Artificial Intelligence: Principles and Techniques

Course assistants:
Amita Kamath

Amita Kamath (Head CA)
Anna Zhu

Anna Zhu
Nicholas Barbier

Nicholas Barbier
Niranjan Balachandar

Niranjan Balachandar
Richard Diehl Martinez

Richard Diehl Martinez
Zhen Qin

Zhen Qin
Andrew Han

Andrew Han
How to contact us: Please use Piazza for all questions related to lectures, homeworks, and projects, and to find announcements. For external queries, emergencies, or personal matters that you don't wish to put in a private Piazza post, you can email us at cs221-sum1819-staff@lists.stanford.edu. For SCPD-specific issues, email scpdsupport@stanford.edu or call 650-741-1542.
Announcements: All announcements will be made on Piazza. NOTE: If you enrolled in this class on Axess, you should be added to the Piazza group automatically, within a few hours. You can also register independently; there is no access code required to join the group.
Calendar: look here for dates/times of all lectures, sections, office hours, due dates.
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):
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: 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.

Finally, to look at course content from the last offering (Spring 2019), click here.

A note on the Summer schedule: CS 221 is also offered during the Fall and Spring quarters, during which classes last ten weeks. In the Summer edition, we will cover the same material, but do so in only eight weeks (with more lecture time per week). Due to this compressed schedule, it is even more important that you have mastered the prerequisite material.
Office Hour Logistics

CA office hours are either in Gates B21 (in the basement) or online. See the calendar for times.

Installing Zoom




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. You are encouraged to use LaTeX to writeup your homeworks (here's a template), but this is not a requirement.
Programming assignments: The grader runs on Python 2.7, which is not guaranteed to work with newer versions (Python 3) or older versions (below 2.7). Please use Python 2.7 to develop your code.

The programming assignments are designed to be run in GNU/Linux environments, such as cardinal.stanford.edu. 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 original grader.py script operating on the student's submission.py) runs on a GNU/Linux server, such as cardinal.stanford.edu.

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

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: 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 Robin or the head CA; the consequences will be much less severe than if we approach you.


Electronic Submission: All assignments are due at 3pm on the due date. Assignments are submitted through Gradescope. 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. If anything goes wrong, please ask a question on Piazza 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.

Late days: An assignment is $\lceil d \rceil$ days late if it is turned in $d$ fractional days late (note that this means if you are $1$ second late, $d = 1/(24 \times 60 \times 60)$ and it is 1 day late). You have seven (7) late days in total that can be distributed among the assignments without penalty. There is a maximum of two (2) late days that can be used per assignment. If you exceed this limit by $k$ hours, then you will incur a multiplicative penalty factor of $\max(1 - k/5, 0)$. For example, if you get $40$ points and turn in your homework 2 days + 1.5 hours after the deadline, then your effective score is $40(1 - 1.5/5) = 28$. If you exceed $5$ hours, you will receive $0$ points. You get a zero after all your late days run out, but we reserve the right to give partial credit in extenuating circumstances.
Regrades: If you believe that the course staff made an objective error in grading, then you may submit a regrade request. 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 Sunday 11:59pm, 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.
Each lecture will be divided into two roughly 50-minute halves covering different material, with a break in between.