A Bayesian and Optimization Perspective
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The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.
1. Introduction 2. Probability and Stochastic Processes 3. Learning in Parametric Modeling: Basic Concepts and Directions 4: Mean-Square Error Linear Estimation 5. Stochastic Gradient Descent: The LMS Algorithm and Its Family 6. The Least-Squares Family 7. Classification: A Tour of the Classics 8. Parameter Learning: A Convex Analytic Path 9. Sparsity-Aware Learning: Concepts and Theoretical Foundations 10. Sparsity-Aware Learning: Algorithms and Applications 11. Learning in Reproducing Kernel Hilbert Spaces 12. Bayesian Learning: Inference and the EM Algorithm 13. Bayesian Learning: Approximate Inference and Nonparametric Models 14. Monte Carlo Methods 15. Probabilistic Graphical Models: Part 1 16. Probabilistic Graphical Models: Part 2 17. Particle Filtering 18. Neural Networks and Deep Learning 19. Dimensionality Reduction and Latent Variables Modeling
Gain an in-depth understanding of all the main machine learning methods, including sparse modeling, online and convex optimization, Bayesian inference, graphical models, deep networks, learning in RKH spaces, dimensionality reduction and dictionary learning