Basic Information About this Subject

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.

Role in EECS curricula

• 6-1 major: EECS elective
• 6-2 major (2022 requirements): EE Systems Science track; EECS elective
• 6-2 major (2017 requirements): Header (sub for 6.034); EECS elective
• 6-3 major (2022 requirements): AID track; EE Systems Science track; EECS elective
• 6-3 major (2017 requirements): Header (sub for 6.034); EECS elective
• New 6-4 major: center subject
• 6-9 major: Header (sub for 6.034)
• Meets with 16.420 (G), which counts as EECS AUS or AAGS in the AI concentration

The combination of this subject and 6.036 will cover almost all of the topics in 6.034 in more technical depth

Role in AeroAstro curriculum

• 16.420 can be used as part of the field exam curriculum for the Autonomous Systems, Controls or Humans in Aerospace fields.
• Meets with 6.4110 (U).

Meeting Time

Monday/Wednesday 9:30 - 11am EST.

Lecture Location

4-370

Office Hours and Problem Session

Office hours are an opportunity to get help with concepts or with particular assignments. We will hold office hours

• Thursday 7--9 PM in 26-328
• Sunday 3--5 PM in 34-301
• Monday 7--9 PM in 26-328

On Fridays at 11AM, we will hold a practice problem-solving session, to work through some tutorial questions and some HW questions. All are welcome! Room is 24-115.

Graduate student section

Students enrolled in the graduate subject, 16.420, will be required to complete additional assignments to differentiate levels. These additional assignments will consist of the following:

• Three mini-projects
• One research paper summary presentation

The research paper summary presentations will be held on Fridays from 10-11am in 33-418 starting on Friday, October 6th. Attendance at the presentations is not mandatory but we will assign two additional discussants to each paper and ask the discussants to attend their assigned presentation.

More information about the graduate student section, as well as the grading rubric for the presentations is available here.

Prerequisites

• 6.4110: One from each group:
  • 6.006 or 16.410 or 6.034
  • 6.041 or 6.008 or 18.05 or 18.600 or 16.09
  • 6.009 or other programming subject at similar level (beyond 6.0001/6.0002)
• 16.420: 16.413 or permission of the instructor.

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)
• Professor Nick Roy (nickroy@csail.mit.edu) -- 16.420 extra section
• TA: Annie Feng (azf@mit.edu)
• TA: Elizabeth Lee (jelizlee@mit.edu)
• TA: Julian Yocum (juliany@mit.edu)
• Grad section TA: Laura Brandt (lebrandt@mit.edu)

Grading scheme

Final grades will be computed as follows, depending on which subject you are registered for.

• 6.4110 (Undergrad subject)

  • 50% problem sets
  • 25% midterm
  • 25% final exam

• 16.420 (Grad subject)

  • 25% problem sets
  • 15% mini-projects
  • 10% research paper presentations
  • 25% midterm
  • 25% final exam

Late policy

• Assignments are due at 11:59:59 PM on the due date.
• All assignments will be submitted via Catsoop.
• The default lateness penalty will be 10% per day.
• 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.

Problem sets

• Will generally be released each Monday and due at 11PM on the following Monday.
• Will generally focus on material from the Monday they are released and the immediately following Wednesday.

Exams

• There will be an in-class midterm on TBD.
• There will be a Final exam, as scheduled by Registrar.

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.

Notation glossary

We will do our best to be consistent with notation throughout the course, following this guide. If you spot a deviation from this guide, please let us know!

Collaboration

Collaboration is encouraged for all assignments. However, everything you hand in on-line or off-line must be your own work. The correct model is to discuss solution strategies and write your solutions individually. Implementations that are identical or differ only by variable names will not receive full credit.

Piazza

We will use Piazza for all announcements and discussions.

Student Support Services

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

GradSupport

For graduate students, a variety of issues may impact your academic career including faculty/student relationships, funding, and interpersonal concerns. In the Office of Graduate Education (OGE), GradSupport provides consultation, coaching, and advocacy to graduate students on matters related to academic and life challenges. If you are dealing with an issue that is impacting your ability to attend class, complete work, or take an exam, you may contact GradSupport by email at gradsupport@mit.edu or via phone at (617) 253-4860.

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

COVID accommodation policy

If you don't feel well, please don't panic, and also don't come to class!

If you are on a Medical Hold due to attesting to potential symptoms, or have tested positive and must isolate, we'll work to be sure you can keep up with everything.

The lectures are being recorded and posted promptly, the homework is available online, and at least some office hours will be held via Zoom, so everything will be available to you.

If you want to talk to a staff member about difficulties this might pose for you, please make a private post on Piazza or mail one of us directly, and we'll do our best to help.

You also can always contact Student Support Services or GradSupport for additional assistance.