course_info:cis_4780

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?