The book explores connections between Riemannian geometry and several well known machine learning techniques, including boosting and logistic regression. In addition to providing motivation for these techniques, the book provides a mechanism for designing new statistical procedures that are shown experimentally to outperform the state-of-the-art. This work follows up on traditional information geometry with a more applied machine learning perspective.