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When & Where
Date: 
Tue, November 27, 2018 - 9:00 AM to 11:00 AM
Location: 
Barrows 356: D-Lab Convening Room
Description
Type: 

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 algorithms including k-nearest neighbor, linear regression, decision tree, random forest, boosting, and ensembling. 

Prior knowledge requirements: R Fundamentals: Parts 1 through 4 or previous intermediate working knowledge of R.

 

Details
Training Host: 
D-lab Facilitator: 
Evan Muzzall
Format Detail: 
hands-on, interactive
Participant Technology Requirement: 
Laptop
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