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Full Description
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts.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.
Contents
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
