Machine Learning

Course Description

This course is about gaining exposure to core machine learning techniques and their applications to business domains and functions. MIT research shows 9% higher top line and 26% higher net margins for companies with ‘Leading Digital’ capabilities. While most firm have capabilities in summarizing the data they have, very few have the analytical abilities to gain true insights from such data to get business results. The course will expose you to the art-of-the-possible with respect to state-of-the-art methods and applications of supervised and unsupervised machine learning. Majority (5/6th) of the course will focus on supervised machine learning for prediction. The course will be based on $1 million plus worth use-cases of analytics completed at the Carlson Analytics Lab. It will be driven practical uses cases and use a mixture of lecture, discussion of key issues, and an in-class group prediction contest over the three days! At the end of the course all students will become excellent at understanding the power of data mining to create business value. You will learn how to identify opportunities of using supervised and unsupervised machine learning methods, setup the problems correctly, develop intuition of how the major classes of machine learning algorithms work, and how to use the appropriate metrics and approaches to judge performance. We will also cover the important topic of algorithmic bias and examine ways to correct for it. Finally we will look at explainable AI and the interface between machine learning and causal inference to estimate heterogeneous treatment effects.

Code

XBA1-GB 8237

Credits

3

Subject

Business Analytics

Programs That Include This Course

• Master's Business Analytics and AI (MS)