Prepare

Due: October 3, 2024

Learning objectives

• Distinguish unsupervised learning from supervised learning
• Define dimension reduction and motivations for its usage
• Introduce principal component analysis (PCA)
• Define uniform manifold approximation and projection (UMAP)
• Implement PCA and UMAP using {recipes}
• Interpret the results of PCA and UMAP

Preparations

📖 Read

• TMWR ch 16 - Dimensionality Reduction
• ISLR ch 12.1-.3 - Unsupervised learning, PCA, and Missing Values and Matrix Completion