Machine Learning: A Bayesian and Optimization Perspective (Hardcover)

Chapter 1. Introduction Chapter 2. Probability and Stochastic Processes Chapter 3. Learning in Parametric Modeling: Basic Concepts and Directions Chapter 4: Mean-Square Error Linear Estimation Chapter 5. Stochastic Gradient Descent: The LMS Algorithm and Its Family Chapter 6. The Least-Squares Family Chapter 7. Classification: A Tour of the Classics Chapter 8. Parameter Learning: A Convex Analytic Path Chapter 9. Sparsity-Aware Learning: Concepts and Theoretical Foundations Chapter 10. Sparsity-Aware Learning: Algorithms and Applications Chapter 11. Learning in Reproducing Kernel Hilbert Spaces Chapter 12. Bayesian Learning: Inference and the EM Algorithm Chapter 13. Bayesian Learning: Approximate Inference and Nonparametric Models Chapter 14. Monte Carlo Methods Chapter 15. Probabilistic Graphical Models: Part 1 Chapter 16. Probabilistic Graphical Models: Part 2 Chapter 17. Particle Filtering Chapter 18. Neural Networks and Deep Learning Chapter 19. Dimensionality Reduction and Latent Variables Modeling