Request Department Approval to Register for a Course
Courses requiring department approval to register
Some MATH, MCS, MTHT, and STAT courses require department approval to register (see spring 2023 list below). If you would like to register for one of these courses please fill out the appropriate form linked below.
Those seeking help or who encounter problems registering for 100/200/300/400 level courses should contact the Director of Undergraduate Studies . Those seeking help registering for 500-level courses should contact the Director of Graduate Studies.
Spring 2023 Courses with Departmental Approval:
MATH: 140, 141, 179, 215, 294, 300, 313, 320, 330, 496
MCS: 275, 320, 360, 361, 401, 421, 425, 471, 472*, 496
STAT: 101 (online section only), 382, 385, 401, 496
MTHT: 401, 411, 465, 466, 468
CS: Please contact the College of Engineering Department of Computer Science.
NOTE: STAT 130 and MATH 170 are restricted to students majoring in Biological Sciences or Integrated Health Studies. If you're not declared as one of those majors, you will need to request approval by sending an email to firstname.lastname@example.org.
*MCS 472 may be taken concurrently with MCS 471 for the spring 2023 term.
Independent Study - MATH, STAT, MCS 496 Heading link
In order to register for independent study credit within the department, students must submit departmental approval from the link above for the appropriate rubric (e.g., MATH 496, STAT 496, MCS 496). Upon completing the departmental approval form, students must submit a syllabus and instructor approval to the undergraduate studies office. Instructor approval can either be an email from the instructor to undergraduate studies or for the student to forward the approval email to undergraduate studies.
The following is a sample syllabus with all of the information needed:
- Course Credit
- 1-4 credits is common*
- Course Professor(s)
- Please list the instructor’s name, title, and contact information
- Course Description
- Example: “The main topic of the course is high-dimensional robust statistics. In this course, we will learn about the recent advancement in high-dimensional robust statistics, including but not limited to the recent works that gave the first polynomial-time robust algorithms for a wide range of statistical problems with dimension-independent error guarantees.”
- Course Objective/Topics
- Example: “Algorithms, Linear Algebra, Corruption Models, Sample-Efficient Robust Estimation, Robust Mean Estimation in Polynomial Time”
- Example: “This course is assessed by reading papers, conducting research meetings with the instructor, writing a summary report by the end of the term, and working on research questions that could lead to publication.”
When determining the amount of credit hours, please use the below formula to select 1-4 credit hours.
*60 work hours = 1 credit hour.
Number of work hours per week x Number of weeks in semester = n; Number of total work hours. Then, n / 60 = Total number of Credit Hours earned.