Techniques in artificial intelligence

Lectures: 2 sessions / week, 1.5 hours / session

Professor: Leslie Pack Kaelbling

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.

We want to strongly encourage collaboration as a way for students to come to understand the material better. You may do the projects in groups of two, turning in a single write-up (or you may do it on your own). However, you may not partner with the same person for more than three project assignments. Projects 1a, 1b, and 1c count as distinct assignments.

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.

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