Event date
Thursday, September 21, 2017
Event time
10:00am to 12:00pm
Barrows 356: Convening Room

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 supervised machine learning including k-nearest neighbor, linear regression, decision tree, random forest, boosting, and ensembling. 
Prior knowledge requirements: R FUN!damentals: Parts 1 through 4 or previous intermediate working knowledge of R.