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SUMMARY:Clustering & Classification (10 of 11): Dimensionality Reduction Techniques (1 of 2)
DESCRIPTION:The Advanced R Series: Clustering & Classification\n\nData 
 rarely comes neatly labeled or structured\, yet patterns still exist—even 
 when they are not immediately obvious. Clustering and classification 
 methods allow researchers to uncover structure in their data\, group 
 similar observations\, and reduce dimensionality without imposing rigid 
 assumptions about the underlying relationships.\n\nThis series introduces 
 researchers to statistical and machine-learning methods for grouping\, 
 modeling\, and interpreting high-dimensional data. Participants will learn 
 a broad range of approaches—from hierarchical and centroid-based models 
 to probabilistic\, fuzzy\, density-based\, graph-based\, and mixed-type 
 clustering techniques—along with strategies for dimensionality reduction 
 and fairness considerations. Emphasis is placed on understanding model 
 assumptions\, evaluating model performance\, and selecting methods that 
 align with the characteristics of the data rather than forcing data to fit 
 inappropriate models.\n\nAll workshops will use R and RStudio\, so some 
 experience with R or other programming languages is encouraged but not 
 required. See the R Fundamentals for Data Analysis for an introduction to R 
 and RStudio. Attendees without prior experience are encouraged to review 
 this content.\n\nDimensionality Reduction Techniques (1 of 2\; workshop 10 
 of 11): Part one introduces key dimensionality-reduction techniques such as 
 PCA and factor models. We discuss variance decomposition\, latent 
 structure\, and interpretability considerations. Participants will learn to 
 perform and interpret these methods in R and understand when dimensionality 
 reduction improves clustering performance.\n\nApplication: Laboratory 
 Data\n\nQuestions? Please reach out to the Centre for Scholarly 
 Communication at csc.ok@ubc.ca.\n\nA full schedule of workshops can be 
 found at csc.ok.ubc.ca/workshops/
LOCATION:LIB 111\, Okanagan - Centre for Scholarly Communication 
ORGANIZER;CN="Centre for Scholarly Communication (CSC)":MAILTO:csc.ok@ubc.ca
CATEGORIES:Data
CONTACT;CN="Centre for Scholarly Communication (CSC)":MAILTO:csc.ok@ubc.ca
STATUS:CONFIRMED
UID:LibCal-3972813
URL:https://libcal.library.ubc.ca/event/3972813
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