Amazon cover image
Image from Amazon.com

Machine learning : a Bayesian and optimization perspective / Sergios Theodoridis

By: Material type: TextTextPublication details: [Place of publication not identifed] : [publisher not identified], c2015Description: 1,075 pages : illustrations ; 24 cmISBN:
  • 9780128015223
Subject(s): LOC classification:
  • Q 325.5 .T44 2015
Contents:
Chapter1. Probability and stochastic processes -- Chapter2. Learning in parametric modeling: basic concepts and directions -- Chapter3. Mean-square error linear estimation -- Chapter4. Stochastic gradient descent: the LMS algorithm -- Chapter5. The least-squares family -- Chapter6. Classification: a tour of the classics -- Chapter7. Parameter learning: a convex analytic path -- Chapter8. Sparsity-aware learning: concepts and theoretical foundations -- Chapter9. Sparcity-aware learning: algorithms and applications -- Chapter10. Learning in reproducing Kernel Hilbert spaces -- Chapter11. Bayesian learning: inference and the EM algorithm -- Chapter12. Bayesian learning: approximate inference and nonparametric models -- Chapter13. Monte Carlo methods -- Chapter14. Probabilistic graphical models: Part I -- Chapter15. Probabilistic graphical models: Part II -- Chapter16. Particle filtering -- Chapter17. Neural networks and deep learning -- Chapter18. Dimensionality reduction and Latent Variables Modeling .
Summary: 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.
Item type: Books
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Books Books National University - Manila LRC - Graduate Studies General Circulation Gen. Ed - CEAS GC Q 325.5 .T44 2015 (Browse shelf(Opens below)) c.1 Available NULIB000014084

Includes index.

Chapter1. Probability and stochastic processes -- Chapter2. Learning in parametric modeling: basic concepts and directions -- Chapter3. Mean-square error linear estimation -- Chapter4. Stochastic gradient descent: the LMS algorithm -- Chapter5. The least-squares family -- Chapter6. Classification: a tour of the classics -- Chapter7. Parameter learning: a convex analytic path -- Chapter8. Sparsity-aware learning: concepts and theoretical foundations -- Chapter9. Sparcity-aware learning: algorithms and applications -- Chapter10. Learning in reproducing Kernel Hilbert spaces -- Chapter11. Bayesian learning: inference and the EM algorithm -- Chapter12. Bayesian learning: approximate inference and nonparametric models -- Chapter13. Monte Carlo methods -- Chapter14. Probabilistic graphical models: Part I -- Chapter15. Probabilistic graphical models: Part II -- Chapter16. Particle filtering -- Chapter17. Neural networks and deep learning -- Chapter18. Dimensionality reduction and Latent Variables Modeling .

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.

There are no comments on this title.

to post a comment.