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When & Where
Fri, February 14, 2020 - 1:00 PM to 4:00 PM
356B Barrows (D-Lab 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.


Training Keywords: 
Data Science, Data Visualization, Machine Learning
Primary Tool: 
Training Host: 
D-lab Facilitator: 
Evan Muzzall
Format Detail: 
Interactive, hands-on
Participant Technology Requirement: 
Laptop required; please install R version 3.4 or greater in advance; the RStudio IDE is recommended but not required. Install all packages in 01-overview.Rmd file

Intermediate to Advanced Competency

These workshops assume that participants have sufficient coding or tool experience to learn a highly focused topic. 

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