Predictive Analytics

Course Code
MBAN 553
2.25 hours
  • Fall 22 (A)
MBAN Students Only

Predictive Analytics --- This course introduces students to a supervised learning approach to building predictive models to inform managerial decision making. The class will build on a foundation of linear regression and logistic regression and extend to machine learning approaches such as decision trees, support vector machines, naive Baynes and neural networks. The course will address related issues such as bias-variance trade-offs (i.e. underfitting vs. overfitting models) and advanced techniques such as ensemble learning and reinforcement learning.

Taught By
Anocha Aribarg
  • Professor of Marketing
Prof. Aribarg’s research interests involve fusing psychology and consumer behavior theories with Bayesian statistical and econometric modeling...
Eric Schwartz
  • Arnold M. and Linda T. Jacob Faculty Fellow
  • Associate Professor of Marketing
Professor Eric Schwartz's expertise focuses on predicting customer behavior, understanding its drivers, and examining how firms actively manage their...