School of Computer Science Wiki

You've loaded an old revision of the document! If you save it, you will create a new version with this data.

Include a brief summary of the course topics and requirements, the general format of the course, and the methods of evaluation.

== Skills and knowledge students should have prior to beginning the course: ==

* Fluency in Java Programming

== Course Topics: ==

* Constraint satisfaction problems
  * Backtracking
  * Iterative improvement
  * Constraint propagation
  * Limitation of logic
  * Notation and axioms of Bayesian probability
  * Inference with JPD
  * Bayesian networks (BNs)
  * Inference in BNs by variable elimination
  * Building models as BNs
  * Markov decision processes
  * Reinforcement learning

== Course Format: ==

* Lecture format:
  * Online materials location and format:
  * Lab or tutorial format and expectations:

== Method of evaluation: ==

* Number of Assignments: 3
  * Number of Graded Labs: NO
  * Number of Quizzes: NO
  * Formal Midterm? NO
  * Course project? NO
  * Final Exam?  YES
  * Group work? NO
  * mostly programming assignments?
  * Written documents?

Edit summary

Note: By editing this page you agree to license your content under the following license: CC Attribution-Noncommercial-Share Alike 4.0 International