Course Description

6.825 is a graduate-level introduction to artificial intelligence. Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning.

This course was also taught as …

6.825 is a graduate-level introduction to artificial intelligence. Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning.

This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5504 (Techniques in Artificial Intelligence).

Learning Resource Types
notes Lecture Notes
assignment Programming Assignments
A graphic showing a Mars Rover-like robot in a sandy, rocky environment. A circular path of words is overlaid, with the words Precepts - Agent - Actions - Environment (and back to Precepts again).
An example of the agent and environment dichotomy. This figure illustrates a robot taking actions that affect the state of the environment then receiving percepts with new information on the environment. (Image courtesy of Beryl Simon.)