CSCE 790.1 Spring 2019 Grading
The course grade will be based on
presentations and a project.
The project will be one of the following:
Implement the CB algorithm in R;
implement Yimin Huang's identifiability algorithm in R;
design and implement an exploratory program for identifiability under
the modest parametric assumptions of ARSV;
write a paper extending or contrasting state-of-the art results;
apply some of the techniques presented in this course to a real non-trivial
data set;
another project of similar scope.
Students will be required to choose a project before spring break; projects
can either be carried out by individuals or small teams of two or three
students. The instructors must approve a proposal submitted at least one
week before spring break, so that the approval is reached before spring
break,
to give students enough time to complete the project.
The grading policy is as follows:
- Presentations: 50%
- Project: 30%
- Final Exam: 20%
The numeric scores are translated to letter grades as follows: [90-100] = A,
[87-90[ = B+, [80-87[ = B, [77-80[ = C+, [70-77[ = C, [67-70[ = D+,
[60-67[ = D, [0-60[ = F.
Some of the homework assignments require use of the Bayesian network shell
Hugin.
Each student is expected to attend all classes for this course and is
responsible for all material covered in class or assigned. In particular,
absence from more than four scheduled classes, whether excused or unexcused,
is excessive and may result in a grade penalty.
Each student must follow the
University Honor
Code