Communication: We will use Ed for all communications, which you can access via Canvas. CGOE students: please email scpd-gradstudents@stanford.edu if you need general assistance. Make a public Ed post whenever possible. For extra sensitive matters, you can email cs221-aut24-lead-staff@lists.stanford.edu, which is visible by only the instructors, head CA, course manager, 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-aut24-lead-staff@lists.stanford.edu by Friday, Oct 11 (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. You may use generative AI tools such as Co-Pilot and ChatGPT as you would use a human collaborator. This means that you may not directly ask generative AI tools for answers or copy solutions, and you must acknowledge generative AI tools as collaborators. The use of generative AI tools to substantially complete an assignment or exam (e.g. by directly copying) is prohibited and will result in honor code violations. We will be checking students' homework to enforce this policy.

Anyone violating the honor code policy will be referred to the Office of Community Standards. 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.

Stanford as an institution is committed to the highest quality education, and as your teaching team, our first priority is to uphold your educational experience. To that end we are committed to following the syllabus as written here, including through short- or long-term disruptions, such as public health emergencies, natural disasters, or protests and demonstrations. However, there may be extenuating circumstances that necessitate some changes. Should adjustments be necessary, we will communicate clearly and promptly to ensure you understand the expectations and are positioned for successful learning.

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:

• (Required) Programming CS 106A, CS 106B
• (Required) Discrete math, mathematical rigor: CS 103
• (Required) Probability: CS 109
• (Required) Linear algebra: Math 51
• (recommended, but not required) Algorithms: CS 161
• (recommended, but not required) Systems: CS 107

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.