Machine learning : a Bayesian and optimization perspective
著者
書誌事項
Machine learning : a Bayesian and optimization perspective
Academic Press, c2015
- : hbk
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
1. Introduction2. Probability and Stochastic Processes3. Learning in Parametric Modeling: Basic Concepts and Directions4: Mean-Square Error Linear Estimation5. Stochastic Gradient Descent: The LMS Algorithm and Its Family6. The Least-Squares Family7. Classification: A Tour of the Classics8. Parameter Learning: A Convex Analytic Path9. Sparsity-Aware Learning: Concepts and Theoretical Foundations10. Sparsity-Aware Learning: Algorithms and Applications11. Learning in Reproducing Kernel Hilbert Spaces12. Bayesian Learning: Inference and the EM Algorithm13. Bayesian Learning: Approximate Inference and Nonparametric Models14. Monte Carlo Methods15. Probabilistic Graphical Models: Part 116. Probabilistic Graphical Models: Part 217. Particle Filtering18. Neural Networks and Deep Learning19. Dimensionality Reduction and Latent Variables Modeling
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