Advanced Artificial Intelligence (Winter 2008)
This is the page for Advanced AI for Winter 2008
Dr. Regli's Office Hours are 14:00-15:00 M-W, right before class.
The syllabus for the course is available here.
Homeworks
Homework #1 is available here.
Homework #2 is available here.
Homework #3 is available here.
Lecture slides
Lecture Unit 1: Introduction to Uncertainty.
Lecture Unit 2: Bayes Nets (Ch 14 from R&N).
Lecture Unit 3: Inference in Bayes Nets (Ch 14 from R&N).
Lecture Unit 4: Kalman Filtering (Not from R&N).
Lecture Unit 5: Particle Filtering (Not from R&N).
Lecture Unit 6: Temporal Probability (Ch 15 from R&N).
Example of the Dishonest Casino (Dynamic Bayes Nets).
Programming Assignment
The programming assignment is going to be given out in 2-phases. The first phase is empirical and experimental. The objective of which is to encourage students to experiment with a number of the techniques taught in the class and employ them in creative ways against a real-world dataset. This is not a toy problem, but an open-ended assignment indented to require creativity on the part of the student. Students are expected to look at the data and scenario and then, using the techniques taught in the class thus far (or look ahead, if you so desire), identify ways in which these techniques might be applied to deduce about properties of the world.
