MACHINE LEARNING FOR DATA-DRIVEN ENGINEERING DESIGN

Prerequisites & course information

Requisites: ( MATH 234 or 376 ), ( MATH 320 , 340 , 341 , or 375 ), and ( I SY E 210 , B M E 325 , E C E 331 , MATH/STAT 310 , 410, STAT 312 , 324 , 333 or 340 ), or graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2025 Learning Outcomes: 1. Identify concepts and techniques for machine learning and data-driven engineering design Audience: Both Grad & Undergrad 2. Demonstrate skills in constructing surrogate models for design problems Audience: Both Grad & Undergrad 3. Demonstrate skills in applying machine learning and data-driven models in design problems Audience: Both Grad & Undergrad 4. Apply the machine learning and data-driven methods to solve engineering design problems Audience: Both Grad & Undergrad 5. Identify mathematics for machine learning and data-driven engineering design Audience: Graduate Learning Outcomes: 1. Identify concepts and techniques for machine learning and data-driven engineering design Audience: Both Grad & Undergrad 2. Demonstrate skills in constructing surrogate models for design problems Audience: Both Grad & Undergrad 3. Demonstrate skills in applying machine learning and data-driven models in design problems Audience: Both Grad & Undergrad 4. Apply the machine learning and data-driven methods to solve engineering design problems Audience: Both Grad & Undergrad 5. Identify mathematics for machine learning and data-driven engineering design Audience: Graduate