BUAD 5082 - Machine Learning II

Fall (3) Murray Prerequisite(s): BUAD 5072

This course is designed to provide students with a comprehensive working knowledge of a family of analytical techniques that have grown out of the Artificial Intelligence, Data Mining and Machine Learning communities over the last several decades. In recent years, these techniques have come into widespread use for business by data scientists, principally because they lend themselves to the discovery of relationships among variables that can only be found by examining the very large quantities of often unstructured data that characterize the world of big data. This course is designed to provide students with a deep, practical understanding of several of the most common and powerful analytical Machine Learning techniques in use by today’s data scientists such as resampling methods, non-linear methods, Tree-Based Methods, Support Vector Machines, Text Mining and, from the Unsupervised Learning family of techniques, Cluster Analysis and Association Analysis.