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SUMMARY:R for Machine Learning Part 1
DESCRIPTION:This is a part of the SummeR of LeaRning Series.\n\nWe'll cover 
 the basics of machine learning in R. Below are the concepts we'll 
 teach:\n\nPart 1:\n\n\n	Machine Learning Basics\n	Train\, Validation\, and 
 Test Data\n	Features vs Labels\n\n\nPart 2:\n\n\n	Unsupervised 
 Learning\n	Clustering\n\n\nPart 3:\n\n\n	Supervised 
 Learning\n	Classification\n	Regression\n\n\nPart 4:\n\n\n	Model 
 Interpretation\n	AI Bias\n\n\n \n\nPrerequisites\n\nYou should be familiar 
 with the materials from:\n\n\n	R for Everyone \n	R for Data 
 Manipulation\n\n\n \n\nInstructor\n\nAlbert Lee is an Instructional 
 Designer and Analyst for the Data Science Initiative at the UCSF Library.
LOCATION:Online
ORGANIZER;CN="Albert Lee":MAILTO:albert.lee8@ucsf.edu
CATEGORIES:Bioinformatics and Statistics, Programming
CONTACT;CN="Albert Lee":MAILTO:albert.lee8@ucsf.edu
STATUS:CONFIRMED
UID:LibCal-9128986
URL:https://calendars.library.ucsf.edu/calendar/events/r4ml_1
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