Description
Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc.- Provides quick tutorial reviews of important and emerging topics of research in signal processing-based tools- Presents core principles in signal processing theory and shows their applications- Discusses some emerging signal processing tools applied in machine learning methods- References content on core principles, technologies, algorithms and applications- Includes references to journal articles and other literature on which to build further, more specific, and detailed knowledge
Table of Contents
1. Introduction to Signal Processing and Machine Learning Theory2. Continuous-Time Signals and Systems3. Discrete-Time Signals and Systems4. Random Signals and Stochastic Processes5. Sampling and Quantization6. Digital Filter Structures and Their Implementation7. Multi-rate Signal Processing for Software Radio Architectures8. Modern Transform Design for Practical Audio/Image/Video Coding Applications9. Discrete Multi-Scale Transforms in Signal Processing10. Frames in Signal Processing11. Parametric Estimation12. Adaptive Filters13. Signal Processing over Graphs14. Tensors for Signal Processing and Machine Learning15. Non-convex Optimization for Machine Learning16. Dictionary Learning and Sparse Representation
