AI Modeling in Practice

1.5 Credits · Level: Graduate

Predictive Analytics is a subfield of business analytics that draws tools and techniques from data mining, machine learning and predictive modeling that aim to create models that can precut outcomes for uncertain events. In this course, we will build on earlier classes on statistical foundations and machine learning to enrich our set of tools for prediction. The first part of the course will include uncovering patterns, features and regularities in data to build powerful models for prediction. Topics will include association rules and dimension reduction. The second part of the course will focus on how businesses integrate these ideas to create models for successful prediction. Topics include regression trees, k-nearest neighbors, artificial neural networks, and support vector machines. This section will also cover ensemble modeling where predictions and classifications are made using combinations of models. The course will focus on the benefits, challenges and pitfalls of applying these concepts in practice and help students think analytically through the process of building, implementing and providing prescriptions from predictive models.