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MACHINE LEARNING IN ACTION FOR INDUSTRIAL ENGINEERS

ISYE 521
Course Description

Principles, algorithms, and industrial engineering applications of machine learning. Predictive analytics, with a focus on combining data and models to improve decision-making. Methods include: statistics, linear regression, logistic regression, regularization, over-fitting, clustering, classification and regression trees, boosting, bagging, deep learning, and neural networks. Applications areas include: healthcare, transportation, and the public sector.

Prerequisites

(COMPSCI 200 , COMPSCI 220 , or place into COMPSCI 300 ),(ISYE 323 or COMPSCI/ECE/ISYE 524 ), and (ISYE 210 , STAT 311 , STAT 324 , MATH/STAT 309 , or MATH/STAT 431 ), grad/prof standing, member of Engr Guest Stdnts, or declared in Capstone Cert in AI for Engr Data Analytics

Satisfies
Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.8

7.29% from Historical

Completion Rate
100%

1.05% from Historical

A Rate
60%

47.69% from Historical

Class Size
10

-73.96% from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

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