CS221: Artificial Intelligence: Principles and Techniques

Communication: We will use Ed for all communications, which you can access via Canvas. SCPD students: please email scpd-gradstudents@stanford.edu if you need general assistance. Make a public Ed post whenever possible. For private matters, make a private Ed post, which will be visible to only the instructors and all CAs. For extra sensitive matters, you can also email cs221-spr2223-staff@lists.stanford.edu, which is visible by only the instructors, head CA, and student liaison.

Video access disclaimer: Lectures and sections will be recorded and available on Canvas. 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 or concerns, please contact us.

Academic accommodations: If you need an academic accommodation, contact the Office of Accessible Education (OAE). The OAE will then prepare an OAE letter with the recommended accommodations. Send this letter to cs221-spr2223-staff@lists.stanford.edu by Friday, April 21 (week 3).

Collaboration policy and honor code: Please read Stanford's honor code policy. In the context of CS221, 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 solution.
Uploading your writeup or code to a public repository (e.g., GitHub) so that it can be accessed by other students.
Looking at solutions from previous years, either official or written up by another student, or found online.

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!). We periodically run similarity-detection software over all submitted student programs, including programs from past quarters and any solutions found online on public websites.

Generative AI Policy: Each student is expected to submit their own solutions to the CS221 Homeworks. The use of generative AI tools such as Co-Pilot and ChatGPT to substantially complete an assignment or exam (e.g by entering exam or assignment questions) is strictly prohibited and will result in honor code violations. We will be checking students' Homework submissions for honor code violations. Generative AI tools will only be allowed for completing the final projects. Please refer to Stanford's Generative AI Policy Guidelines for more details.

Anyone violating the honor code policy will be referred to the Office of Judicial Affairs. If you think you violated the policy (it can happen, especially under time pressure!), please reach out to us; the consequences will be much less severe than if we approach you.

Inclusion: The CS221 teaching staff is committed to creating an inclusive and supportive learning environment for all students. Please be respectful to your fellow students, course CAs, and instructors. If you see any problems, please reach out to us early.

What is this course about? The goal of artificial intelligence (AI) is to tackle complex real-world problems with rigorous mathematical tools. In this course, you will learn the foundational principles and practice implementing various AI systems. Specific topics include machine learning, search, Markov decision processes, game playing, constraint satisfaction, graphical models, and logic.

Prerequisites: This course is fast-paced and covers a lot of ground, so it is important that you have a solid foundation in a number of areas. Here are the basic skills that you need and the classes that teach those skills:

Programming (ideally Python): CS 106A, CS 106B, CS 107
Discrete math, mathematical rigor: CS 103
Probability: CS 109
Linear algebra: Math 51

It is less important that you know particular things (e.g., we don't use eigenvectors in this course even though that's a pillar of any linear algebra course), and more important that you've done enough related things that you feel at ease with it. While it is possible to fill in the gaps, this course does move quickly, and ideally you want to be focusing your energy on learning AI rather than catching up on prerequisites. We have made a few prerequisite modules that you can review to refresh your memory, and the first homework (foundations) will allow you to also get some practice on these basics.

Further reading: There are no required textbooks 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 great resources:

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 (free online).
Hastie, Tibshirani, and Friedman. The Elements of Statistical Learning. Covers machine learning from a rigorous statistical perspective (free online).
Tsang. Foundations of Constraint Satisfaction. Covers constraint satisfaction problems (free online).

Note that some of these books use different notation and terminology from this course, so it may may take some effort to make the appropriate connections.

Homeworks (60%): There are weekly homeworks. Each homework is usually centered around an application and has both written and programming parts.

Homeworks must be typeset and submitted on Gradescope as a PDF. The coding portion will be autograded; you get only feedback on a subset of the test cases. Autograding is strict, so check your outputs carefully! Read more about the policies around late days (7 total, max 2 per homework), regrades, and submission.

Exams (40%):
- Exam 1 (20%): Wednesday, 5/10 at 6-8 PM PT.
- Exam 2 (20%): Friday, 6/9 at 3:30-5:30 PM PT.
Both exams will be closed-book and closed-source, except you are allowed one double-sided sheet (US letter or A4) of notes.

Non-SCPD Students: Since final exam times are scheduled by the registrar, we will not be accommodating alternate final exam times. All enrolled, non-SCPD students must take the final exam in person on the given date.

SCPD Students: SCPD students will need to nominate exam monitors for both exams and coordinate the exam process with the SCPD exams team. Please refer to this link for more information on the process. For any additional questions, please reach out to the SCPD exams team.
Project (optional, up to 2% extra credit): Use the tools from class to build something interesting of your choice!
- Projects should be done in groups of 1-4 students.
- Each project group will be assigned a CA mentor who will give feedback and answer questions.
- For inspiration, check out previous CS221 projects.
- See the project page for more details.
The project will be something that you work on throughout the quarter and we have set up some milestones to help you along the way:
- Project interest form [p-interest] (due April 21)
- Project proposal [p-proposal] (due May 5)
- Project progress report [p-progress] (due May 19)
- Project final report and video [p-final] (due June 5)

Ed (up to 2% extra credit): Please help answer your classmates's questions on Ed! You will be awarded extra credit depending on how substantial and helpful you were on Ed.

	Monday	Tuesday	Wednesday	Thursday	Friday	Saturday	Sunday
Week 1	Apr 3	Apr 4	Apr 5	Apr 6	Apr 7	Apr 8	Apr 9
Homework [Foundations] Module [Prerequisites] [Machine Learning I]	Lecture: Introduction (Sanmi) 10:30am-11:50am		Lecture: Machine Learning I (Sanmi) 10:30am-11:50am		Problem Session 3:00pm-4:20pm [Solutions] [Video]
Week 2	Apr 10	Apr 11	Apr 12	Apr 13	Apr 14	Apr 15	Apr 16
Homework [Sentiment] Module [Machine Learning II]	Lecture: Machine Learning II (Sanmi) 10:30am-11:50am	Foundations HW due	Lecture: Machine Learning III (Sanmi) 10:30am-11:50am		Problem Session 3:00pm-4:20pm [Problems] [Solutions] [Video]
Week 3	Apr 17	Apr 18	Apr 19	Apr 20	Apr 21	Apr 22	Apr 23
Homework [Route] Module [Search]	Lecture: Search (Moses) 10:30am-11:50am	Sentiment HW due	Lecture: Search & Heuristics (Moses) 10:30am-11:50am		Final Study List Deadline Problem Session 3:00pm-4:20pm [Problems] [Solutions] [Video]
Week 4	Apr 24	Apr 25	Apr 26	Apr 27	Apr 28	Apr 29	Apr 30
Homework [Blackjack] Module [MDPs]	Lecture: MDPs I (Moses) 10:30am-11:50am	Route HW due	Lecture: MDPs II (Moses) 10:30am-11:50am		Problem Session 3:00pm-4:20pm [Problems] [Solutions] [Slides] [Video]
Week 5	May 1	May 2	May 3	May 4	May 5	May 6	May 7
Homework [Pacman] Module [Games]	Lecture: Games I (Moses) 10:30am-11:50am	Blackjack HW due	Lecture: Games II (Moses) 10:30am-11:50am		Problem Session 3:00pm-4:20pm [Problems] [Solutions] [Video] Project Proposal due
Week 6	May 8	May 9	May 10	May 11	May 12	May 13	May 14
Module [CSPs]	Lecture: Factor Graphs (Sanmi) 10:30am-11:50am		Lecture: Beam Search (Moses) 10:30am-11:50am Exam I: 6pm-8pm		Problem Session 3:00pm-4:20pm [Problems] [Solutions] [Slides] [Video]
Week 7	May 15	May 16	May 17	May 18	May 19	May 20	May 21
Homework [Scheduling] Module [Bayesian Nets]	Lecture: Bayesian Nets I (Sanmi) 10:30am-11:50am	Pacman HW due	Lecture: Bayesian Nets II (Sanmi) 10:30am-11:50am		Problem Session 3:00pm-4:20pm [Problems] [Solutions] [Slides] [Video] Project Progress Report due
Week 8	May 22	May 23	May 24	May 25	May 26	May 27	May 28
Homework [Car] Module [Bayesian Nets III / Logic I]	Lecture: Bayesian Nets III (Sanmi) 10:30am-11:50am	Scheduling HW due	Lecture: Logic I (Moses) 10:30am-11:50am		Course Withdrawal / Change of Grading Basis Deadline Problem Session 3:00pm-4:20pm [Problems] [Solutions] [Slides] [Video]
Week 9	May 29	May 30	May 31	Jun 1	Jun 2	Jun 3	Jun 4
Homework [Logic] Module [Logic II]	Memorial Day	Car HW due	Lecture: Logic II (Moses) 10:30am-11:50am		Problem Session 3:00pm-4:20pm [Problems] [Solutions] [Slides] [Video]
Week 10	Jun 5	Jun 6	Jun 7	Jun 8	Jun 9	Jun 10	Jun 11
	Lecture: Conclusion 10:30am-11:50am Project Final Report due	Logic HW due	Lecture: Exam review session 10:30am-12:20pm [Problems] [Solutions] [Video]		Exam II: 3:30-5:30pm