Summary

This week, we will introduce unsupervised learning.

Learning Objectives

After completing this week, you are expected to be able to:

  • Understand the difference between supervised and unsupervised machine learning tasks.
  • Identify supervised and unsupervised machine learning tasks.
  • Understand and identify unsupervised learning subtasks: dimension reduction, clustering, density estimation, and outlier detection.
  • Use principal components analysis (PCA) for dimension reduction.
  • Use k-means and other methods for clustering.
  • Use kernel density estimation and mixture models for density estimation.
  • Use isolation forest for outlier detection.

Topics

  • Unsupervised Learning
    • Dimension Reduction
      • PCA
    • Clustering
      • k-Means
      • Agglomerative Clustering
      • DBSCAN
    • Density Estimation
      • Kernel Density Estimation
      • Gaussian Mixture Models
    • Outlier Detection
      • Isolation Forest

Activities

Upcoming Deadlines