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Machine Learning A Bayesian and Optimization Perspective 1st Edition by Sergios Theodoridis ISBN 0128015225 9780128015223

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Machine Learning A Bayesian and Optimization Perspective 1st Edition Sergios Theodoridis Digital Instant Download

Author(s): Sergios Theodoridis
ISBN(s): 9780128015223, 0128015225
Edition: 1
File Details: PDF, 34.48 MB
Year: 2015
Language: english
SKU: EB-5138754 Category: Tag:

Machine Learning A Bayesian and Optimization Perspective 1st Edition by Sergios Theodoridis – Ebook PDF Instant Download/Delivery: 0128015225, 9780128015223

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Product details:

ISBN 10: 0128015225

ISBN 13: 9780128015223 

Author: Sergios Theodoridis

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.

Table of contents:

  1. Probability and Stochastic Processes

  2. Learning in Parametric Modeling: Basic Concepts and Directions

  3. Mean-Square Error Linear Estimation

  4. Stochastic Gradient Descent: The LMS Algorithm and Its Family

  5. The Least-Squares Family

  6. Classification: A Tour of the Classics

  7. Parameter Learning: A Convex Analytic Path

  8. Sparsity-Aware Learning: Concepts and Theoretical Foundations

  9. Sparsity-Aware Learning: Algorithms and Applications

  10. Learning in Reproducing Kernel Hilbert Spaces

  11. Bayesian Learning: Inference and the EM Algorithm

  12. Bayesian Learning: Approximate Inference and Nonparametric Models

  13. Monte Carlo Methods

  14. Probabilistic Graphical Models: Part I

  15. Probabilistic Graphical Models: Part II

  16. Particle Filtering

  17. Neural Networks and Deep Learning

  18. Dimensionality Reduction and Latent Variables Modeling

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