SMA 5504 Techniques in Artificial Intelligence

Course Staff

Leslie Pack Kaelbling          email:

Wynne Hsu                           email:

Liu Bing                               email:

Lecture Hours

        Monday       3-4pm  VC Room

        Wednesday 8-10pm VC Room

        Friday         2-3pm   VC Room



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.


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 homework assignments and two exams. The homeworks will count for 50% of the grade, and the exams, 50%.


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.