Delivery: Live and virtual

Explore where machine learning fits into contemporary organizational research. Designed primarily for doctoral students building their methods foundation

What to Expect

This tutorial introduces the fundamental concepts and methods of machine learning, with an emphasis on supervised learning. Participants will examine how machine learning models learn from data, how model complexity affects performance, and how regression-based methods support more advanced techniques such as neural networks. Through conceptual explanations and worked examples, the tutorial will build a practical foundation for understanding model selection, regularization, and prediction. Participants will apply these concepts to a dataset by fitting and comparing regularized regression models, evaluating their performance, and identifying signs of overfitting and underfitting.

Presenter

Dr. Louis Hickman, Virginia Tech

Dr. Louis Hickman is an Assistant Professor of Management at Virginia Tech, a Visiting Scholar at Amazon, and a Senior Fellow at University of Pennsylvania’s Wharton People Analytics. He holds an M.S. in Computer and Information Technology, specializing in natural language processing, and a Ph.D. in Industrial-Organizational Psychology. His research focuses on applications and implications of machine learning, natural language processing, and artificial intelligence in selection, assessment, and training and development. His research won the Personnel Psychology best paper award and twice won the Society for Industrial-Organizational Psychology’s Jeanneret Award for Excellence in the Study of Individual or Group Assessment.

Register for the tutorial series

One registration includes all four live tutorials, their released resources, and related program activities.