Explores noise reduction techniques in electrical systems, covering concepts like Fourier transform, impedance matching, and dithering.
Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Covers the basics of image processing for microscopy, including acquiring, correcting defects, enhancing images, and extracting information.
