Announcements
References
Includes pointers to required reading not in the textbook
and suggested exercises.
6.825 is a graduatelevel introduction to artificial intelligence.
Topics include: representation and inference in firstorder logic; modern
deterministic and decisiontheoretic planning techniques; basic supervised
learning methods; and Bayesian network inference and learning.
 6.041 (Probabilistic Systems Analysis)
 6.042 (Mathematics for Computer Science)
 6.046 (Introduction to Algorithms) (desirable, but not
required)
Students should be familiar with uninformed search algorithms
(depthfirst and breadthfirst 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 four
takehome project assignments and two exams: a quiz and
a final. The projects
will count for 50% of the grade (5% for Proj 0, 15% each of Projs 1, 2, 3), and the exams, 50% (20%
for the quiz and 30% for the final).
Late Policy
for Projects: 10% off for each calendar day late. No credit if
more than 5 days late.
We want to strongly encourage collaboration as a way
for students to come to understand the material better. Projects 0 and 1 are
individual assignments, but you may do Project 2 and Project 3 in
groups of two, turning in a single writeup. You do not have to
partner with the same person for both of the projects and you can
choose to do either or both of them on your own.
If you are looking for a partner for an assignment, email
the class list asking if anyone is available. You are also quite welcome
to discuss the assignments as much as you'd like between groups. The ultimate
requirement is this: Don't put your name on anything you don't understand.
There will, of course, be no collaboration allowed on the exams.
