Professor: Leslie Pack Kaelbling
Two sessions / week
1.5 hours / session
Includes pointers to required reading not in the textbook and suggested exercises.
6.825 is a graduate-level introduction to artificial intelligence. Topics 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.
Students should be familiar with uninformed search algorithms (depth-first and breadth-first methods), discrete probability (random variables, expectation, simple counting), propositional logic (boolean algebra), basic algorithms and data structures, basic computational complexity, and basic calculus. Students should also be aware that course assignments will require the use of the Java® programming language.
The work for this course will consist of 4 take-home project assignments and two exams. The projects will count for 50% of the grade, and the exams, 50%. Late Policy for Projects: 10% off for each calendar day late. No credit if more than 5 days late.