Strategic Insight from Machine Learning

Introduction

Digitalization creates high speed, high volume digital activity traces – Big Data. Using recent advances in machine learning, organizations can use these data to build models that predict specific events in real-time. Managers can use machine learning-based analytics to improve a wide variety of tasks ranging from targeted marketing to preventative maintenance to fraud detection. The course emphasizes defining viable machine learning problems and assessing machine learning models. The class will function like a lab in which students get hands-on experience with cutting-edge analytics processes, tools, and techniques. This firsthand experience provides students with insight into how big data analytics can (and can't) be used for business processes and decision-making.

Course content

This is an intensive, in-person, 4-day course. Before the course begins, students must read a background text to ensure a common vocabulary and familiarity with basic techniques. Most of the course will occur like a workshop, in which we collectively explore tools and reflect on their strengths, weaknesses, and potential strategic application. Because discovery/surprise are so important in this process, most slides and materials will NOT be made available in advance of class.

Before the class, the professor will post several short videos. These cover crucial statistical background knowledge, and must be watched in advance of the class.

Day 1: Defining ML and Isolating ML-suitable problems
We will spend day 1 specifying what machine learning (ML) is, what it can do, and how it interfaces with the larger organization. We will answer questions including: What is machine learning (ML)? How is ML distinct from classic scientific inquiry? From other elements of the cognitive computing universe? What are the fundamental techniques/algorithms in ML? With this definition of ML and its techniques, to what types of problems does the toolset lend itself? How does ML interface with the organization’s larger digital ecosystem?

Day 2: Data, Modeling, and Model Assessment I
This day will present the “nitty gritty” of how machine learning is done – we will explore how the algorithms and data model work. We will begin the day reviewing data storage, collection, and transformation. We will then execute some modeling exercises stepwise before a discussion about what constitutes a “good” model. This discussion will cover important technical topics such as overfit, cross-validation, and target leak. Finally, we will introduce automated ML.

Day 3: Data, Modeling, and Model Assessment II
In day 3, we will introduce more tools to conduct machine learning, which we will use to conduct several illustrative machine learning projects. Distinguishing model evaluation from model valuation, we will introduce techniques to assign value to models, and discuss that as a bridge between granular model output and higher-level strategic insight.

Day 4: Reflecting High-Level Implications of Modeling Nuances
On day 4, we will discuss sources of cultural and institutional resistance to ML adoption. We will have a final student exercise, in which students apply their learning and present strategic insights from a machine learning project.