MACHINE LEARNING FOR BUSINESS ANALYTICS

GENBUS 656

Course Description

Introduction to machine learning techniques in business. Focus on applications for solving business problems, including hands-on practice in the context of various real-world data sets. Materials covered include machine learning foundations, different methodological approaches, and implementation tools for machine learning for business applications. Methods include both supervised learning techniques (linear regression and classification, non-linear regression, CARTs, random forests, SVMs, artificial neural nets, etc.) as well as unsupervised learning techniques (clustering, principal components, etc.).

Prerequisties

GENBUS 307 , GENBUS 317 704, 705, ECON 400 , ECON 410 , MATH/STAT 310 , STAT 333 , STAT 340 , or declared in the Business Exchange program

Satisfies

ACTSCI 657 , MARKETNG 779 , ECON/FINANCE 320 , FINANCE 330 , ACTSCI 655 , and ACTSCI 654

Credits

2 to 3

Offered

Not Applicable

Grade Point Average

3.8

3.93% from Historical

Completion Rate

99.31%

0.02% from Historical

A Rate

73.1%

38.3% from Historical

Class Size

145

86.43% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

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