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When & Where
Date: 
Mon, June 29, 2020 - 9:00 AM to 12:00 PM
Location: 
Remote (Zoom information forthcoming)
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 FUN!damentals: Parts 1 through 4 or previous intermediate working knowledge of R.

  

Keyword: 
Primary Tool: 
R
Details
Training Host: 
Format Detail: 
Remote, hands-on, interactive
Participant Technology Requirement: 
Laptop, Internet connection, Zoom account

Beginner

Designed for those who have no experience and are just beginning their coding, tool or method journey.

Examples: Programming Fundamentals, Git Fundamentals, Stata Fundamentals, R Fundamentals, Python Fundamentals, Basics of Excel, Tableau Fundamentals

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