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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 using the tidymodels framework.

Date & Time

Date:
April 19, 2021
Time:
1:00pm - 4:00pm

Website

Event Details

Organizer

D-Lab

Event Category

Event Categories:
Professional Development Events

Location

Online via Zoom

Venue

Online via Zoom