Course Description
This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving …
This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. In addition, it covers applications of decision trees, neural nets, SVMs and other learning paradigms.
Course Info
Learning Resource Types
notes
Lecture Notes
group_work
Projects
![A decision tree from chapter 4 of the lecture notes.](/courses/6-034-artificial-intelligence-spring-2005/8683b76db3f615e0e8717bbbe2379002_6-034s05.jpg)
An example of a decision tree from chapter 4 (Learning Introduction) of the lecture notes section. (Image by Prof. Tomás Lozano-Pérez and Prof. Leslie Kaelbling.)