CSCE 582: Grading

The course grade will be based on homework, Bayesian network development exercises, midterm exams, a final exam, and a programming project. The tentative grading policy for undergraduate students is as follows:

Test 1: 7.5%
Test 2: 7.5%
Homework assignments: 12.5%
Programming project: 7.5%
Bayesian network development exercises using Hugin: 30%
Final Exam: 35%

Graduate students are required to present papers from the literature in class. Presentations will be done in teams of two or three students, using overhead transparencies or computer slides. The tentative grading policy for graduate students is as follows:

Test 1: 8.75%
Test 1: 8.75%
Homework assignements: 10%
Programming project: 2.5% (2.5% extra credit)
Bayesian network development exercises using Hugin: 30%
Final Exam: 35%
Presentation of a paper from the recent literature: 5%

The precise grading policy has not yet been determined. Some of the homework assignments require use of a Bayesian network shell, such as Hugin. The graduate students will be required to present papers from the recent research literature and their grade will reflect the quality of their presentations. Here are examples of papers suitable for presentation by graduate students: Horvitz, Eric, et al. "The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users." Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98), pp. 256-265. Lauritzen, Steffen L., et al. "Diagnostic Systems by Model Selection: A Case Study." Selecting Models from Data: Artificial Intelligence and Statistics IV, pp.143-152. (Cheeseman and Oldford, eds.) Springer, 1994. Monti, Stefano and Giuseppe Carenini. "Dealing with Expert Inconsistency in Probability Elicitation." IEEE Transactions on Knowledge and Data Engineering, 12, 4 (July/August 2000), pp.499-508.