AEM 3275

AEM 3275

Course information provided by the 2026-2027 Catalog.

This course aims to provide business majors with essential machine learning concepts and practical skills. Through a blend of theory and hands-on experiences, you'll learn how to utilize data-driven insights in the business world. The focus is on analyzing data effectively, improving prediction performance, and extracting valuable information for managerial decision-making. We'll apply machine learning to diverse business contexts, including predicting customer behavior, forecasting prices, and natural language processing. Each application involves specific machine learning tasks like classification, numeric prediction, and clustering. We'll tackle these tasks using various models, such as logistic regressions, support vector machines, decision-trees, ensemble learning (e.g., random forests and boosting), and neural networks.


Prerequisites AEM 2840 or equivalent.

Last 4 Terms Offered 2026SP, 2025SP

Learning Outcomes

  • Identify opportunities and challenges associated with machine learning in various business contexts.
  • Implement different machine learning models, and evaluate the model performance.
  • Interpret and visualize analytical conclusions and insights.
  • Design machine learning based solution to business context problems.

View Enrollment Information

Syllabi: none
  •   Regular Academic Session.  Combined with: AEM 5275

  • 3 Credits Opt NoAud

  •  3750 AEM 3275   LEC 001

    • TR
    • Jan 25 - May 11, 2027
    • Zhang, J

  • Instruction Mode: In Person

Syllabi: none
  •   Regular Academic Session.  Combined with: AEM 5275

  • 3 Credits Opt NoAud

  •  3751 AEM 3275   LEC 002

    • TR
    • Jan 25 - May 11, 2027
    • Zhang, J

  • Instruction Mode: In Person