Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Explores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.
Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.
