SCIENTIFIC MACHINE LEARNING
Introduction to scientific machine learning and its application to physical problems. Mathematical and numerical models of physical systems are integrated with machine learning approaches to develop physics-based or numerics-based data-driven methods for the solution of science and engineering problems. Graduate/Undergraduate Equivalency: CMOR 517 . Mutually Exclusive: Cannot register for CMOR 417 if student has credit for CMOR 517 .
Prerequisites & course information
Short Title: SCIENTIFIC MACHINE LEARNING Department: Comp Appl Math Operations Rsch Grade Mode: Standard Letter Course Type: Lecture Credit Hours: 3 Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students. Course Level: Undergraduate Upper-Level Prerequisite(s): CMOR 304 and ( CMOR 303 or CMOR 302 )
Sources
- Course catalogue ↗2026–2027