Machine learning : a Bayesian and optimization perspective /
Theoridis, Sergios.
Machine learning : a Bayesian and optimization perspective / Sergios Theodoridis - [Place of publication not identifed] : [publisher not identified], c2015 - 1,075 pages : illustrations ; 24 cm.
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.
9780128015223
MACHINE LEARNING
Q 325.5 .T44 2015
Machine learning : a Bayesian and optimization perspective / Sergios Theodoridis - [Place of publication not identifed] : [publisher not identified], c2015 - 1,075 pages : illustrations ; 24 cm.
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.
9780128015223
MACHINE LEARNING
Q 325.5 .T44 2015