Machine Learning: A Bayesian and Optimization Perspective, Second Edition, gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches based on optimization techniques combined with the Bayesian inference approach. The book builds from the basic classical methods to recent trends, making it suitable for different courses, including pattern recognition, statistical/adaptive signal processing, and statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. In addition, sections cover major machine learning methods 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 and supported by examples and problems, giving an invaluable resource to both the student and researcher for understanding and applying machine learning concepts. This updated edition includes many more simple examples on basic theory, complete rewrites of the chapter on Neural Networks and Deep Learning, and expanded treatment of Bayesian learning, including Nonparametric Bayesian Learning. Presents the physical reasoning, mathematical modeling and algorithmic implementation of each methodUpdates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modelingProvides case studies on a variety of topics, including protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, and more INDICE: 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. Montel 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
- ISBN: 9780128188033
- Editorial: Academic Press
- Encuadernacion: Rústica
- Páginas: 1160
- Fecha Publicación: 06/03/2020
- Nº Volúmenes: 1
- Idioma: Inglés