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Course Staff
Leslie Pack Kaelbling
email: lpk@ai.mit.edu
Wynne Hsu
email: whsu@comp.nus.edu.sg
Liu Bing
email: liub@comp.nus.edu.sg
Lecture Hours
Monday 3-4pm VC Room
Wednesday 8-10pm VC Room
Friday 2-3pm
VC Room
References
Description
SMA5504 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.
Prerequisites
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.
Grading
The work for this course will consist of 4 homework assignments and two
exams. The homeworks will count for 50% of the grade, and the exams,
50%.
Collaboration
We want to strongly encourage collaboration as a way for students to
come to understand the material better. You may do the homeworks 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 homework assignments. If you are looking for a partner for an
assignment, email the TA immediately after the assignment is handed out,
and we'll try to introduce you to someone. 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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