MACHINE LEARNING IN ACTION FOR INDUSTRIAL ENGINEERS

Prerequisites & course information

Requisites: ( COMP SCI 200 , 220 , or placement in COMP SCI 300 ),( I SY E 323 or I SY E/COMP SCI/E C E 524 ), and ( I SY E 210 , STAT 311 , 324 , STAT/MATH 309 , 409, 431 , or MATH 331 ), grad/prof standing, member of Engr Guest Stdnts, or declared in AI for Engr Data Analytics Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Identify problems amenable to machine learning and the techniques required to solve those problems (regression vs. classification, regularization, bagging vs. boosting, etc.) Audience: Both Grad & Undergrad 2. Apply appropriate analytical models to solve problems and improve decision-making using Python Audience: Both Grad & Undergrad 3. Effectively communicate findings through both oral and written formats Audience: Both Grad & Undergrad 4. Demonstrate an understanding of how industrial engineering techniques (e.g., optimization) are used to train machine learning models Audience: Graduate Learning Outcomes: 1. Identify problems amenable to machine learning and the techniques required to solve those problems (regression vs. classification, regularization, bagging vs. boosting, etc.) Audience: Both Grad & Undergrad 2. Apply appropriate analytical models to solve problems and improve decision-making using Python Audience: Both Grad & Undergrad 3. Effectively communicate findings through both oral and written formats Audience: Both Grad & Undergrad 4. Demonstrate an understanding of how industrial engineering techniques (e.g., optimization) are used to train machine learning models Audience: Graduate