Matrix analysis is the study of matrices and their algebraic properties. Some particular topics out of many include; operations defined on matrices (such as matrix addition, matrix multiplication and operations derived from these), functions of matrices (such as matrix exponentiation and matrix logarithm, and even sines and cosines etc. of matrices), and the eigenvalues of matrices (eigendecomposition of a matrix, eigenvalue perturbation theory). Matrices can be studied in different ways. They are a linear algebraic structure and have a topological/analytical aspect and they also carry an order structure that is induced by positive semidefinite matrices. The interplay of these closely related structures is an essential feature of matrix analysis. The book treats some aspects of analysis related to matrices including such topics as matrix monotone functions, matrix means, majorization, entropies, quantum Markov triplets. There are several popular matrix applications for quantum theory. This book emphasizes on these aspects of matrix analysis from an efficient analysis point of view. Applications include such areas as signal processing, systems and control theory, statistics, Markov chains, and mathematical biology. Introduction to Matrix Analysis and Applications is appropriate for an advanced graduate course on matrix analysis. It can also be used as a reference for researchers in quantum information, statistics, engineering and economics.