SCIENTIFIC COMPUTING AND MACHINE LEARNING FOR ENGINEERING APPLICATIONS
Key computational topics for engineering applications will be discussed, encompassing both established classical numerical methods and the emerging field of machine learning. Knowledge of calculus [such as MATH 221 ], linear algebra and differential equations [such as MATH 320 ], probability [such as MATH 331 ] and programming in Python or MATLAB [such as COMP SCI 220 ] is required.
Prerequisites & course information
Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Implement representative machine learning methods for regression analysis to solve engineering problems Audience: Graduate 2. Apply both classical numerical methods and machine learning techniques to solve Ordinary Differential Equations (ODEs) and Partial Differential Equations (PDEs) Audience: Graduate 3. Evaluate the strengths and weaknesses of classical numerical methods and machine learning techniques for solving engineering problems Audience: Graduate 4. Develop reduced-order models to accelerate computation Audience: Graduate Learning Outcomes: 1. Implement representative machine learning methods for regression analysis to solve engineering problems Audience: Graduate 2. Apply both classical numerical methods and machine learning techniques to solve Ordinary Differential Equations (ODEs) and Partial Differential Equations (PDEs) Audience: Graduate 3. Evaluate the strengths and weaknesses of classical numerical methods and machine learning techniques for solving engineering problems Audience: Graduate 4. Develop reduced-order models to accelerate computation Audience: Graduate
Sources
- Course catalogue ↗2026-2027