January 2027 Live Online Short Course

Machine Learning

Ivan Hernandez

Course Description

Organizations often have vast datasets with a multitude of variables and structures. While their scale and complexity can make extracting meaning difficult, these datasets hold immense potential for addressing applied organizational challenges, including drawing inferences about current employees, and forecasting future outcomes. Traditional statistical analyses are often limited in these predictive capabilities. In contrast, machine learning offers a formal approach for improving and understanding the generalizability of predictions. We will use state-of-the-art Python machine learning libraries to equip students with foundational skills and example code for executing the complete predictive modeling lifecycle, including formatting and structuring datasets, comparing and selecting among common predictive algorithms, and building reusable prediction pipelines. The workshop will emphasize developing critical thinking about one’s analyses. Students will be equipped to evaluate the practical trade-offs between different predictive models, describe the strengths and limitations of multiple performance metrics, and determine when to finalize trained models to generate predictions for new, unobserved cases. Overall, students will come away with a practical toolkit for building, evaluating, and deploying predictive models in organizational research, while exploring a wide range of applied examples demonstrating how predictive analytics can address real-world organizational needs.

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Meet the Instructor

Dr. Ivan Hernandez is an Associate Professor and Area Head of Industrial-Organizational Psychology at Virginia Tech. He is currently an Associate Editor for the Journal of Applied Psychology and an editorial board member for Organizational Research Methods and Technology, Mind, and Behavior. His work has been published in leading research journals, including Industrial-Organizational Psychology: Perspectives on Science and Practice, Organizational Research Methods, Psychological Methods, Behavior Research Methods, Personnel Psychology, and the Journal of Applied Psychology. He served as team lead of the winning team in the 2023 SIOP Machine Learning Competition and co-organized the 2024, 2025, and 2026 SIOP Machine Learning Competitions.

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