Machine Learning

Introduction

Digitalization creates high speed, high volume digital activity traces – Big Data. Using these data and recent advances in Machine Learning (ML), organizations can automatically build predictive models and tools that can improve a wide variety of routines, ranging from targeted marketing to preventative maintenance to fraud detection. But adopting these tools requires: specifying viable problems, recognizing underlying data requirements and shortcomings, understanding models’ potential failure points, assessing model performance, translating model predictions to actionable business insights, and reflecting on the ethical and security implications of data collection/model building. To successfully deploy ML/AI, managers must become familiar and comfortable with foundational tasks. Managers do not need to become data scientists, but managers in digital organizations need to understand what ML can and cannot do, what ML requires, how to formulate viable ML questions, how to action data-driven predictions, and how models can break.

This course develops these foundational skills through hands-on experience with cutting edge analytics tools and techniques, and through case discussions designed to reflect on the technical foundations of ML/AI successes and failures.

Course content

Course outline (on an overall level)

• Machine learning’s location within the analytics landscape
• Data structures, and the correspondence between data and ML tasks
• Specifying viable and valuable ML problems
• Modeling processes and techniques
• Automated ML
• ML case studies, reflecting on the technical underpinnings