Please note: This is a two-part workshop series. The first session will occur Thursday, September 15 from 2:00pm to 4:00pm. The second session will occur Friday, September 16 from 2:00pm to 4:00pm. If you are registered for the September 15 session, your registration is valid for both days.
Machine Learning often evokes images of Skynet, flying cars, and computerized homes. However, these are less science fiction as they are tangible phenomena that are predicated on description, classification, and pattern recognition in data. To social scientists, such patterns might be critical for investigating evolutionary relationships, global health patterns, voter turnout in local elections, or individual psychological diagnoses.
This two-part workshop introduces some of the basics of the ‘caret’ and ‘SuperLearner’ R packages for algorithm creation, model training and tuning, and visualization of results.
Prior knowledge requirements: R for Data Science: Parts 1 through 4 or previous intermediate working knowledge of R.
