Lectures related to Singular Value Decomposition (SVD)

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

Explores the decomposition of Markov chains into communicating classes and the behavior of long-run averages.

Covers the analysis of beams with concentrated loads, continuous load distributions, and matching conditions for frames and arches.

Explores Runge Kutta and multistep methods for solving ODEs, including Backward Euler and Crank-Nicolson.