Dec 22, 2024  
2020-2021 Graduate Catalog 
    
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2020-2021 Graduate Catalog [ARCHIVED CATALOG]

BIA 6201 - Statistics & Machine Learning

(2)

This course is divided into two sections. The first section provides an introduction to some of the most important concepts in applied statistics such as probability distributions (Normal, Z, t and Chi-Square), sampling theory, and hypothesis testing. The second section of the course covers basic concepts in machine learning (supervised and unsupervised learning, training versus test sets, overfitting) and predictive analytic techniques with a focus on multiple linear regression and logistic regression. Other topics may include classification and decision trees methods. Examples are drawn from economics, finance, marketing and operations research.

Prerequisite: A previous course in statistics and BIA 6311 or consent of the Program Director