TY  - BOOK
AU  - Theodoridis,Sergios
TI  - Machine learning : : a Bayesian and optimization perspective 
SN  - 9780128188033
AV  - Q325.5 THE
PY  - 2020///
CY  - London
PB  - Elsevier/Academic Press, 
KW  -  Machine learning.
KW  - Mathematical optimization.
KW  - Bayesian statistical decision theory.
N1  - Includes bibliography; 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
N2  - "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"--Publisher's website
ER  -