4. Unsupervised learning

• 4.1. Gaussian mixture models
  • 4.1.1. GMM classifier
• 4.2. Clustering
  • 4.2.1. Affinity propagation
  • 4.2.2. Mean Shift
  • 4.2.3. K-means
  • 4.2.4. Spectral clustering
• 4.3. Decomposing signals in components (matrix factorization problems)
  • 4.3.1. Principal component analysis (PCA)
    • 4.3.1.1. Exact PCA and probabilistic interpretation
    • 4.3.1.2. Approximate PCA
  • 4.3.2. Independent component analysis (ICA)