Section outline

  • Lecture: Unsupervised learning: Clustering and Data reduction algorithms
    Clustering algorithms – K-means, Silhouette measure, Mixture models, hierarchical clustering – measures for choosing number of clusters. Data reduction algorithms – Principal components analysis (PCA) and Independent components analysis (ICA) -- measures for choosing number of components

    Faculty: John Kornak

    Please watch 2019 recorded lectures

    • Session Slides:

    • MachineLearningInR unsupervisedV1 File
    • MachineLearningInR unsupervisedV1 File

    • Session Audio/Video Recording (Access restricted to registered students):

    • Watch 2019 Recording URL
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    • Assignment Due Date:  June 3rd, 2020

    • Project Assignment

    • Assignment Answer Key (Access restricted to registered students):