I Temporal ICA Models -- 1 Hidden Markov Independent Component Analysis -- 2 Particle Filters for Non-Stationary ICA -- II The Validity of the Independence Assumption -- 3 The Independence Assumption: Analyzing the Independence of the Components by Topography -- 4 The Independence Assumption: Dependent Component Analysis -- III Ensemble Learning and Applications -- 5 Ensemble Learning -- 6 Bayesian Non-Linear Independent Component Analysis by Multi-Layer Perceptrons -- 7 Ensemble Learning for Blind Image Separation and Deconvolution -- IV Data Analysis and Applications -- 8 Multi-Class Independent Component Analysis (MUCICA) for Rank-Deficient Distributions -- 9 Blind Separation of Noisy Image Mixtures -- 10 Searching for Independence in Electromagnetic Brain Waves -- 11 ICA on Noisy Data: A Factor Analysis Approach -- 12 Analysis of Optical Imaging Data Using Weak Models and ICA -- 13 Independent Components in Text -- 14 Seeking Independence Using Biologically-Inspired ANN's.