Signal Processing and Machine Learning Theory (Paperback)

1. Introduction to Signal Processing and Machine Learning Theory
2. Continuous-Time Signals and Systems
3. Discrete-Time Signals and Systems
4. Random Signals and Stochastic Processes
5. Sampling and Quantization
6. Digital Filter Structures and Their Implementation
7. Multirate Signal Processing for Software Radio Architectures
8. Modern Transform Design for Practical Audio/Image/Video Coding Applications
9. Discrete Multi-Scale Transforms in Signal Processing
10. Frames in Signal Processing
11. Parametric Estimation
12. Adaptive Filters
13. Signal Processing over Graphs
14. Tensors for Signal Processing and Machine Learning
15. Non-convex Optimization for Machine Learning
16. Dictionary Learning and Sparse Representation