Signal filtering is an important stage in the signal processing chain, which involves manipulating a signal to raise, modify, or suppress certain aspects. Signal filtering is crucial in various fields, such as telecommunications, audio, image, and biomedical signal processing, noise reduction, equalization, and system identification, to name a few. Adaptive filtering is an advanced approach in which the signal processing technique adjusts its parameters over time to optimize its handling of varying input conditions. The key idea is to dynamically adapt the filter's coefficients, allowing it to respond to changes in the input signal and enhance its effectiveness in real-world scenarios. Adaptive filtering has lately witnessed a wide range of advancements in the concept and at the application level, making up more and more approaches and subcategories of this discipline. For instance, sparse adaptive filters focus on efficiently utilizing only essential information, reducing computational complexity. This makes sparse Bayesian learning and compressed sensing contribute to more effective and faster adaptive filtering in scenarios with large datasets. Another application-driven dimension is distributed adaptive filtering, which has expanded with the rise of distributed systems and networked environments. Distributed adaptive filtering techniques are adapted for decentralized scenarios, allowing for collaborative processing and learning across multiple nodes while maintaining adaptability. One rapidly growing approach that must be integrated with adaptive filtering techniques is Deep Learning. Integrating deep learning techniques, particularly neural networks, has enhanced adaptive filtering capabilities. Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are applied to learn and adapt to complex patterns in data and signals. Advances in adaptive filtering bring more hot topics such as nonlinear adaptive filters, robust adaptive filtering, as well as online learning and real-time adaptation. This book aims to highlight and bring together recent advancements in adaptive filtering techniques and associated applications. It is meant to provide a complete and rich collection of all topics in this ever-changing field.