Machine Learning: A Bayesian And Optimization Perspective by Sergios Theodoridis

Machine Learning: A Bayesian And Optimization Perspective

bySergios TheodoridisEditorSergios Theodoridis

Hardcover | March 27, 2015

not yet rated|write a review

Pricing and Purchase Info

$134.86 online 
Earn 674 plum® points

In stock online

Ships free on orders over $25

Not available in stores


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.

  • All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods.
  • The latest trends: 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 modeling.
  • Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied.
  • MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.

About The Author

Sergios Theodoridis is Professor of Signal Processing and Machine Learning in the Department of Informatics and Telecommunications of the University of Athens.He is the co-author of the bestselling book, Pattern Recognition, and the co-author of Introduction to Pattern Recognition: A MATLAB Approach.He serves as Editor-in-Chief for the...

Details & Specs

Title:Machine Learning: A Bayesian And Optimization PerspectiveFormat:HardcoverDimensions:1062 pages, 9.41 × 7.24 × 0.98 inPublished:March 27, 2015Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0128015225

ISBN - 13:9780128015223

Customer Reviews of Machine Learning: A Bayesian And Optimization Perspective


Extra Content

Table of Contents

Chapter 1. Introduction Chapter 2. Probability and Stochastic Processes Chapter 3. Learning in Parametric Modeling: Basic Concepts and Directions Chapter 4: Mean-Square Error Linear Estimation Chapter 5. Stochastic Gradient Descent: The LMS Algorithm and Its Family Chapter 6. The Least-Squares Family Chapter 7. Classification: A Tour of the Classics Chapter 8. Parameter Learning: A Convex Analytic Path Chapter 9. Sparsity-Aware Learning: Concepts and Theoretical Foundations Chapter 10. Sparsity-Aware Learning: Algorithms and Applications Chapter 11. Learning in Reproducing Kernel Hilbert Spaces Chapter 12. Bayesian Learning: Inference and the EM Algorithm Chapter 13. Bayesian Learning: Approximate Inference and Nonparametric Models Chapter 14. Monte Carlo Methods Chapter 15. Probabilistic Graphical Models: Part 1 Chapter 16. Probabilistic Graphical Models: Part 2 Chapter 17. Particle Filtering Chapter 18. Neural Networks and Deep Learning Chapter 19. Dimensionality Reduction and Latent Variables Modeling