Presented at SPSP 2021 Annual Convention


Machine learning algorithms accommodate big data of the sort that traditional statistics cannot. This session introduces attendees to broadly applicable principles of machine learning: e.g., bias vs. variance tradeoffs, robust prediction and clustering, and quantitative vs. text-oriented analyses. R code and sample APA write-up will be provided to equip and inspire those attending.


Speakers: Fred Oswald, Rice University