Mathematical and Statistical Foundations

Chapter 1: Mathematical Preliminaries (Linear algebra, calculus, optimization, differential geometry) Chapter 2: Probability and Statistics (Distributions, Bayesian inference, hypothesis testing, KL divergence) Chapter 3: Signal and Image Processing Basics (Convolution, Fourier transforms, wavelets, filtering, noise models)