CS221: Artificial Intelligence: Principles and Techniques

Course staff:


Course assistants:

Aaron Effron (Head TA)

Susanna Baby

Jennie Chen

Richard Diehl Martinez

Chuma Kabaghe

Yianni Laloudakis

Jaebum Lee

Nitya Mani

Benjamin Petit

Sudarshan Seshadri

Greg Soh

Pranav Sriram

Anna Zhu

Magdy Saleh

Jon Kotker

Hao Wang

Cecilia Liu
How to contact us: Please use Piazza for all questions related to lectures, homeworks, and projects. For SCPD students, email scpdsupport@stanford.edu or call 650-741-1542.
Calendar: look here for dates/times of all lectures, sections, office hours, due dates.
Grades: click here to check your grades.
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.
Office Hour Logistics

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. You will receive one (1) bonus point for submitting a typed written assignment (e.g. LaTeX, Microsoft Word). We will accept scanned handwritten assignments but they will not receive the bonus point. Once again, the homeworks are due at 11pm.
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 cardinal.stanford.edu, and must submit their assignments from cardinal.stanford.edu as well. The final grading will be run on GNU/Linux servers.

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


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 eight (8) late days in total that can be distributed among the assignments (except for p-poster, p-peer, and p-final) 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 and regrades for homework are due exactly 1 week from grade release (at 11.59pm). 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. If you do choose to submit a regrade request, please submit it through gradescope. Any requests submitted over email or in person or through piazza will be ignored. Note that we may regrade your entire submission, so that depending on your submission you may actually lose more points than you gain.