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Explores market response functions, flash crashes, correlation estimation, and noise filtering in finance.
Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.
Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.
Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
Covers efficient data exploitation through clustering methods and the optimization of market returns using asset clustering.
Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
