Explores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.
Covers Oja's rule in Neurorobotics, focusing on learning eigenvectors and eigenvalues for capturing maximal variance.
Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.
Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
