4. Unsupervised learning¶
- 4.1. Gaussian mixture models
- 4.1.1. Expectation-maximization
- 4.1.2. Variational inference
- 4.1.3. The Dirichlet Process
- 4.1.3.1. GMM classifier
- 4.1.3.2. Variational Gaussian mixtures: VBGMM classifier
- 4.1.3.3. Infinite Gaussian mixtures: DPGMM classifier
- 4.2. Manifold learning
- 4.3. Clustering
- 4.4. Decomposing signals in components (matrix factorization problems)
- 4.5. Covariance estimation
- 4.6. Hidden Markov Models