6.S058 Representation and Inference in Artificial Intelligence (meets with 16.420)

SHARE:

Undergraduate Level
(Meets with 16.420)
Units: 3-0-9
Prerequisites: 6.006 and (6.008 or 6.041) and 6.009
Instructors: Profs. Leslie Kaelbling (lpk@csail.mit.edu and Tomas Lozano-Perez (tlp@mit.edu)
Schedule: MW9:30-11, room E25-111
More information at: https://mit-6s058.github.io
 
Description
 
An introduction to representations and algorithms for artificial intelligence, exclusive of machine learning.  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, basic decentralized and game-theoretic models.
 
ROLE IN CURRICULUM:

  • Pilot version of subject designed for proposed AI+D major
  • Can substitute for 6.034 as a header in 6-2 and 6-3 (requires petition but guaranteed to be accepted)
  • Counts as an EECS subject for 6-1, 6-2, and 6-3, even if you have already taken 6.034
  • Counts as a CS12 subject for 6-2 students
  • 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