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All Projects → dlab-berkeley → Unsupervised-Learning-in-R

dlab-berkeley / Unsupervised-Learning-in-R

Licence: other
Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).

Programming Languages

r
7636 projects

Labels

clusteringdimensionality-reductionunsupervised-learningumaphdbscanisolation-forestsglrmlatent-class-analysis

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Unsupervised Learning in R

Unsupervised machine learning is a class of algorithms that identifies patterns in unlabeled data, i.e. without considering an outcome or target. This workshop will describe and demonstrate powerful unsupervised learning algorithms used for clustering (hdbscan, latent class analysis, hopach), dimensionality reduction (umap, generalized low-rank models), and anomaly detection (isolation forests). Participants will learn how to structure unsupervised learning analyses and will gain familiarity with example code that can be adapted to their own projects.

Author: Chris Kennedy

Prerequisites

This is an intermediate machine learning workshop. Participants should have significant prior experience with R and RStudio, including manipulation of data frames, installation of packages, and plotting.

Prerequisite workshops

Recommended workshops

Technology requirements

Participants should have access to a computer with the following software:

Initial steps for participants

To prepare for the workshop, please download the materials and work through the package installation in 0-install.Rmd. Please report any errors to the GitHub issue queue.

There is also an RStudio Cloud workspace that can be used.

Reporting errors or giving feedback

Please create a GitHub issue to report any errors or give feedback on this workshop.

Resources

Books

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