Dear colleagues,
I am delighted to make you aware of an upcoming educational opportunity that may be of interest to statisticians, data scientists, students, and others interested in applied machine learning.
The ASA Traveling Course, "Tree-Based Machine Learning Methods: Prediction, Inference, and Variable Selection with the randomForestSRC Ecosystem" will be offered online on September 11, 2026, from 9:00 a.m. to 1:00 p.m.
This four-hour workshop provides a practical, code-centered introduction to tree-based machine learning methods using R. Topics will include random forests for regression, classification and survival analysis; out-of-bag inference and prediction; variable selection; class imbalance and imputation; and several advanced tree-based methods.
The course will be led by Hemant Ishwaran and Min Lu from the Division of Biostatistics at the University of Miami, both of whom have extensive experience developing and applying random forest methodology and software.
I encourage anyone interested in learning more about these methods-or expanding their practical machine-learning toolkit-to consider registering.
Please see the attached flyer for additional details and registration information below, and feel free to share this opportunity with colleagues or students who may be interested.
Tree-Based Machine Learning Methods Workshop Tickets, Friday, September 11 • 9 AM - 1 PM EDT | Eventbrite
Best regards,
Tony Panzarella (President, Southern Ontario Toronto Chapter, ASA)
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Tony Panzarella
Assistant Professor
Dalla Lana School of Public Health, University of Toronto
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