Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Explores market response functions, flash crashes, correlation estimation, and noise filtering in finance.
Covers efficient data exploitation through clustering methods and the optimization of market returns using asset 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.
