Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data. To social scientists, such methods might be critical for investigating evolutionary relationships, global health patterns, voter turnout in local elections, or individual psychological diagnoses.
We will discuss basic features of machine learning and how to use R to responsibly apply k-Nearest Neighbor, decision trees, linear regression, and boosting to your own research.
Prior knowledge requirements: R FUN!damentals: Parts 1 through 4 or previous intermediate working knowledge of R.
Please note: This is a two-part workshop series. The first session will occur Monday, March 6 from 10:00am to 12:00pm. The second session will occur Friday, March 10 from 10:00am to 12:00pm. Please remember to register for both days.
