Machine learning : a Bayesian and optimization perspective /

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"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"...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Theodoridis, Sergios, 1951- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam [Netherlands] : Academic Press, 2015.
Temas:
Machine learning.
Mathematical optimization.
Bayesian statistical decision theory.
Apprentissage automatique.
Optimisation mathématique.
Théorie de la décision bayésienne.
Bayesian statistical decision theory
Machine learning
Mathematical optimization
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Probability and stochastic processes
  • Learning in parametric modeling: basic concepts and directions
  • Mean-square error linear estimation
  • Stochastic gradient descent: the LMS algorithm
  • The least-squares family
  • Classification: a tour of the classics
  • Parameter learning: a convex analytic path
  • Sparsity-aware learning: concepts and theoretical foundations
  • Sparcity-aware learning: algorithms and applications
  • Learning in reproducing Kernel Hilbert spaces
  • Bayesian learning: inference and the EM alogrithm
  • Bayesian learning: approximate inference and nonparametric models
  • Monte Carlo methods
  • Probabilistic graphical models: Part I
  • Probabilistic graphical models: Part II
  • Particle filtering
  • Neural networks and deep learning
  • Dimensionality reduction
  • Appendix A LInear algebra
  • Appendix B Probability theory and statistics
  • Appendix C Hints on constrained optimization.

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