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Course Description

An introduction to representations and algorithms for artificial intelligence. Topics covered include: constraint satisfaction in discrete and continuous problems, logical representation and inference, Monte Carlo tree search, probabilistic graphical models and inference, planning in discrete and continuous deterministic and probabilistic models including MDPs and POMDPs.

Meeting Time

Monday/Wednesday 11:00am - 12:30pm EST.

Lecture Location

45-230

Office Hours and Problem Session

Office hours are an opportunity to get help with concepts or with particular assignments. We will hold office hours, starting in the week of Feb. 9.

We will hold a problem session on Fridays at 11AM. It will be somewhere between a recitation and office hours: the TAs will work through some homework problems and/or extra problems with the students who are there and interesting. They will also be available for more high-level questions about course content as well as questions about homework.

Prerequisites

One from each group:

  • Intermediate programming: 6.101
  • Introductory algorithms: 6.121 or 16.410
  • Introductory probability: 6.3700 or 6.3800 or 18.05 or 18.600

Practically speaking, this class will be difficult if you do not have a reasonable programming background, and do not have experience implementing complex data structures (e.g., search trees) and do not have experience with discrete and continuous probability.

Teaching Staff

  • Professor Leslie Kaelbling (lpk@csail.mit.edu)
  • Sunshine Jiang (GTA) – sunsh16e@mit.edu
  • David Koplow (GTA) – dkoplow@mit.edu
  • Divya Shyamal (GTA) – dshyamal@mit.edu
  • Ellery Stahler (GTA) – ellerys@mit.edu
  • Anushka Aggarwal (UTA) – anushka6@mit.edu
  • Alicia Lin (UTA) – ayl27@mit.edu
  • Tiffany Wang (UTA) – twangst@mit.edu
  • Ephraim Wu (UTA) – filbertw@mit.edu

Grading scheme

Final grades will be computed as follows:

  • 10% homework on catsoop
  • 15% mini-projects
  • 45% three quizzes
  • 30% final exam

The role of the homework is to help you learn! If you do 75% of it, you will get full credit. It is on the cat-soop system and completely auto-graded. Select the parts that you find most helpful. Doing more will help you learn more but will not earn further credit.

There will be some time spent in the lectures discussing problem formulations. These discussions are important and their content will be reflected in the quizzes and exam.

Mini-project assignments will require handing in written answers via Gradescope. Their content will be reflected in the quizzes and exam.

Late policy

  • Assignments are due at 11:59:59 PM on the due date.
  • The lateness penalty will be 10% per day on homework; 20% per day on mini-projects.
  • In addition, you have a bank of 10 waivers for late days. You do not need to formally ask to use a late waiver --- at the end of the semester, the course staff will retroactively waive up to 10 late penalties in whatever fashion maximizes your grade.
  • There will be no further extensions for any reason, except illness and personal difficulties, documented via S3.

Textbooks

We will be using the following textbooks:

  • "Artificial Intelligence: A Modern Approach, 4th US ed." by Stuart Russell and Peter Norvig. Website. Unfortunately this textbook is not available for free online. The 3rd edition is widely available, but it's missing a lot of content we will be using and the chapter numbers don't match up. If it's difficult for you to purchase a book, contact lpk.

  • "Algorithms for Decision Making" by Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray. This textbook is available for free online: Website.

Collaboration with humans and use of AI tools

Collaboration is encouraged for all assignments. Use pset partners to find folks to work with. Use homework for active and engaged learning. You are welcome to use any tools you want, for help on understanding the concepts and doing homework, But remember to hold your own understanding to a high standard, because we will test it rigorously in the exams.

Piazza

We will use Piazza for all announcements and discussions.

Student Support Services

Please don't come to class if you are ill!

If you are dealing with a personal or medical issue that is impacting your ability to attend class, complete work, or take an exam, you should contact a dean in Student Support Services (S3). S3 is here to help you. The deans will verify your situation, provide you with support, and help you work with your professor or instructor to determine next steps. In most circumstances, you will not be excused from coursework without verification from a dean. Please visit the S3 website for contact information and more ways that they can provide support.

Website

Disability and Access Services

MIT is committed to the principle of equal access. Students who need disability accommodations are encouraged to speak with Disability and Access Services (DAS), prior to or early in the semester so that accommodation requests can be evaluated and addressed in a timely fashion. If you have a disability and are not planning to use accommodations, it is still recommended that you meet with DAS staff to familiarize yourself with their services and resources. Please visit the DAS website for contact information.

If you have already been approved for accommodations, course staff are ready to assist with implementation. Please inform course staff, who will oversee accommodation implementation for this course.

Website