course_info:cis_4780
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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?
course_info/cis_4780.1425260685.txt.gz · Last modified: 2015/03/02 01:44 by judi