Content
1) Principal Component Analysis, Kernel-PCA
2) Independent Component Analysis (Infomax, FastICA, Second Order Blind Source Separation)
3) Stochastic Optimization
4) Clustering, Embedding, and Visualisation (Central and Pairwise Clustering, Self-Organizing Maps, Locally Linear Embedding)
5) Density Estimation, Mixture Models, Expectation-Maximization Algorithm, Hidden Markov Model
6) Estimation Theory, Maximum Likelihood Estimation, Bayesian Model Comparison