Class materials

Slides

Recorded classes

Attachments

• Overview
• Datasets and data matrix
• Exploratory data analysis
• EDA demo
• Principal component analysis
• PCA demo
• Basic distributed inference
• Matrix sketching
• Kernel methods
• Generalized low-rank models
• Nonlinear dimensionality reduction: ISOMAP and t-SNE
• t-SNE demo
• GLRM demo
• Probabilistic PCA
• Clustering
• Demo for clustering with k-means and Gaussian mixture models
• Spectral clustering demo