A Computational Approach to Statistical Learning (Paperback, 1)

Matrix Methods. Direct solutions to linear systems. Iterative linear model solutions. Iteratively reweighted least squares. Blockwise techniques. Convex optimization. Quasi-Newton and gradient descent. Interior point method. Proximal algorithms. Coordinate descent. Active sets and path solutions. Other techniques. Expectation maximization. Model featurization. Neighborhood prediction. Spectral learning. Stochastic techniques.