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
  * (CIS*3750 or CIS*3760) CIS*3490, (CIS*2460 or STAT*2040), CIS*3700


== 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?