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When & Where
Mon, September 28, 2020 - 1:00 PM to 4:00 PM
Tue, September 29, 2020 - 1:00 PM to 4:00 PM
Remote (Zoom information forthcoming)

*NOTE: Due to limited resources and staff, we are only able to offer workshops to UC Berkeley affiliates, partners (LBL, UCSF), and invited guests. We respectfully ask you not to register if you are not affliliated with UCB, LBL or UCSF.*



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.

Training Keywords: 
Data Manipulation and Cleaning, Data Science, Data Visualization
Primary Tool: 
Training Learner Level: 
Intermediate to Advanced Competency
Training Host: 
Format Detail: 
Remote, hands-on, interactive
Participant Technology Requirement: 
Laptop, Internet connection, Zoom account
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