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Introduction to Machine Learning in R (Workshop 1 of 2) [D-Lab]

November 26 @ 9:00 am - 11:00 am

See organizers’ website for details and registration.

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

Date:
November 26
Time:
9:00 am - 11:00 am
Event Category:
Website:
http://dlab.berkeley.edu/training/introduction-machine-learning-r-part-1-3

Organizer

D-Lab

Venue

356 Barrows Hall
Barrows Hall
Berkeley, CA 94720 United States
+ Google Map
Website:
http://www.berkeley.edu/map?barrows