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. Multi-rate 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