We took it on ourselves to write a book with a simple and unified approach to different AVM techniques. We decided to implement all these techniques using the popular FDTD numerical approach. This will hopefully help the largest possible number of researchers understand and use these techniques. The approach adopted in this book is to first explain the theory in detail and with as much clarifications as possible. Then we move to implement the theory on simple examples using MATLAB. We give extensive codes with detailed explanation to clarify any confusing points. The readers will realize that published results that use more complex structures can be implemented using the supplied codes after making some changes. The target here is to stress and illustrate the basic concepts rather than handle complex structures. We discuss in this book problems that utilize different types of materials and responses. We show how to estimate the sensitivities of scalar objective functions, transient responses, and frequency-domain responses such as the S-parameters. We address problems with isotopic, anisotropic, dispersive, and nonlinear materials. We solve problems with 1D, 2D, or 3D FDTD solvers. Our target is to show the reader the universal nature of the adjoint sensitivity techniques and the wide applications it can be utilized in. The book does not assume prior knowledge of the FDTD technique or of sensitivity analysis approaches. Chapter 1 gives a simple introduction to AVM approaches using simple electric circuits. Chapter 2 is dedicated to give an introduction to the FDTD methods and its application in modeling different types of materials. Chapter 3 introduces the foundation needed to apply the AVM approach using FDTD. The other chapters apply the AVM approach to estimate firstand/or second-order sensitivities of simple high-frequency structures with different types of materials and for different responses. The codes presented in this book are for educational purposes. They can be optimized to further improve the memory storage and the computational time.