Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Explores neurobiological signal processing, covering spike modeling, signal classification, and data characterization using principal component analysis.
