Senate Bill 23-24-13
| Bill ID: | 23-24-13 |
| PCC ID: | 23010 |
| Name: | PCC Proposal to Establish a Master of Science in Applied Machine Learning |
| Proposed: | 09/29/2023 |
| Sponsor: | PCC Committee |
| Proposal: | The College of Computer, Mathematical, and Natural Sciences proposes to establish a Master of Science in Applied Machine Learning. This program exists currently as an iteration of the Master of Professional Studies (MPS) program. The 30-credit MPS program (titled Machine Learning) has been in operation since the Winter 2019-2020 term. Master of Professional Studies programs were first approved in 2005, when the University System of Maryland Board of Regents and Maryland Higher Education Commission approved an expedited review process for master’s and graduate certificate programs that respond quickly to the changing market needs of working professionals. Once a new iteration of the MPS is approved through campus PCC review, it only needs approval by the USM Chancellor to become official. A limitation of offering this program as an MPS iteration is that all Professional Studies programs must use the same generic Federal Classification of Instructional Programs (CIP) code rather than a CIP code that accurately describes the program content. Those who search for academic programs by using the CIP codes related to Machine Learning will not find this program. Moreover, some CIP codes are designated as “STEM” eligible by the US Department of Homeland Security, and international students with F1 visas who graduate from STEM designated programs may continue to work in the United States for two years longer than students in non-STEM designated programs. The generic CIP code for Professional Studies programs does not qualify as STEM-designated, even if the academic content of the Professional Studies program is STEM-related, as is the case with this program. Consequently, the college proposes to transition the current program from a Master of Professional Studies program to a stand-alone Master of Science program in order for the program to be classified more accurately. The 30-credit curriculum will remain the same. The Master of Science in Applied Machine Learning will provide students with the opportunity to engage in cutting edge technical course work in machine learning and develop their problem-solving skills in the art and science of processing and extracting information from data with special emphasis on large amounts of data (Big Data). Students will build solid foundations in mathematics, statistics and computer programming, and explore advanced topics in machine learning such as deep learning, optimization, big data analysis, and signal/image understanding. The program consists of 18 credits of required courses and 12 credits of electives. This program is a non-thesis program and will have both an in-person and distance education version. Graduates of the program will be able to understand the fundamental concepts of machine learning and explain applied mathematics and statistics necessary for the thorough understanding of machine learning algorithms and methods. Students will be able to implement problem-solving and analytical skills necessary to succeed in industry, including scripting and programming, and will be familiar with state-of-the-art machine learning tools and high-performance computing platforms. Students will be able to work in teams to solve problems and demonstrate written and oral communication skills appropriate to engineering professionals. The proposal was approved by the Graduate School PCC committee on September 27, 2023, and the Senate Programs, Curricula, and Courses committee on October 6, 2023. |
| Active? | Yes |
Status
| Status: | Complete |
| Related Files: |
| Status: | Complete |
| Reviewer: | Senate |
| Received: | 2023-10-25 |
| Decision Date: | 2023-11-01 |
| Decision: | The Senate voted to approve this proposal. |
| Next Step: | Presidential Approval, MHEC Approval, Chancellor Approval |
| Related Files: |
| Status: | Complete |
| Reviewer: | Senate Executive Committee (SEC) |
| Received: | 2023-10-13 |
| Decision Date: | 2023-10-20 |
| Decision: | The SEC voted to place the proposal on the agenda of the November 1, 2023 Senate meeting for consideration |
| Next Step: | Senate Review |
| Related Files: |
| Status: | Under Review |
| Reviewer: | Programs, Curricula, & Courses (PCC) Committee |
| Received: | 2023-09-29 |
| Decision Date: | 2023-10-06 |
| Decision: | The PCC Committee voted to approve the proposal. |
| Actions: | The committee considered this proposal at its meeting on October 6, 2023. |
| Next Step: | SEC Review |