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:

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