Overview
Description
This course introduces several important modeling approaches for solving decision-making problems. The first part of the course focuses on statistical learning. The second part of the course introduces machine learning and decision-making under uncertainty.
Requirements
Prerequisites
- Admission to the MSBA program
BAN 701
Original catalog text
Prerequisites
Admission to the MSBA program; BAN 701.
Units
Lecture3
Catalog Details
Offering
Offered: Every Spring
Terms
spring
Restrictions
Admission to the MSBA program
Attributes
Standard
Learning Outcomes
- describe the importance of inference and prediction and distinguish them. Describe supervised and unsupervised learning methods and distinguish them. Interpret the model findings.
- contrast different statistical and machine learning methods.
- identify and describe the challenges in real-world data analytics projects.
- identify and describe “good” vs. “bad” models by virtue of evaluation metrics.
- identify a challenge/ shortcoming in each of the statistical methods discussed in this course AND find and describe a solution from the current research to address/alleviate the problem.