Time/location:

• Lectures: Tue/Thu 9-10:15am in NVIDIA Auditorium
• Sections: will happen based on need; see calendar
• Office hours: see calendar

Instructor: Percy Liang

Course assistants:

• Gabor Angeli
• Naran Bayanbat
• Thiraphat Charoensripongsa
• Rohan Kamath
• Tiffany Low
• Tyler O'Neil
• Aju Thalappillil Scaria
• Cameron Schaeffer
• Hao Su

Contact: Please use Piazza for all questions related to lectures, homeworks, and projects. For private questions, email cs221-aut1213-staff@lists.stanford.edu.

Calendar: look here for dates/times of all lectures, sections, office hours, due dates.

Course description: Problems in game playing, natural language processing, computer vision, robotics are challenging due to the inherent noise/uncertainty and computational complexity. This course provides the mathematical and algorithmic framework for tackling these sorts of problems. Topics include search, decision theory, graphical models, machine learning, and various applications.

Book: Russell and Norvig. Artificial Intelligence: A Modern Approach, 3rd. edition.

Prerequisites: a good grasp of basic data structures and algorithms, probability, linear algebra; solid programming skills.

Coursework and grading:

• Homeworks (30%): there will be weekly homeworks. All homework problems must be submitted online by 11:59pm. No late homeworks will be accepted, but the lowest homework will be dropped.
• Projects (30%): there will be two programming projects (to be done individually) and one open-ended project (done in groups of up to three). No late projects will be accepted.
• Midterm (20%): open-book/open-notes, 3 hours. Date/time: Wed Oct 24 6-9pm. Location: Cemex Auditorium. If you have a conflict, submit a request to take it at another time (do this by Sep 30).
  • A set of practice problems and their solutions are available
• Final exam (20%): open-book/open-notes, 3 hours. Date/time: Mon Dec 10 12:15-3:15pm. Location: NVIDIA AUDITORIUM
• A set of practice problems and their partial solutions are available
• Check my grade: Enter your sunetid below, and we will send you an email with your current course grade, as it exists in our gradebook.
  • Your sunetid is the first part of your email address. For example, if your email is 'abc@stanford.edu' then your sunetid would be 'abc'.
  • The scores from this system may lag behind regrades. If they are not accurate even after the next assignment has been released, please let us know. For example, if you submit a regrade for homework i, and it is still incorrect when homework i+1 is released, please let us know.
  sunetid:

Collaboration policy: we encourage students to form study groups and discuss homeworks and projects. However, students must write up homeworks and code from scratch independently without referring to any notes from the joint session.

All lectures are taped and can be watched online for SCPD students and full-time students.