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 autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.
Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
