Machine learning : a Bayesian and optimization perspective / Sergios Theodoridis

By:

Theodoridis, Sergios, 1951-

Material type: Text

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9780128188033

Subject(s):

LOC classification:

Q325.5 THE

Contents:

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.

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"--Publisher's website.

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Holdings
Item type	Current library	Home library	Shelving location	Call number	Copy number	Status	Barcode
Books	Harare Institute of Technology Main Library	Harare Institute of Technology Main Library	General Collection	Q325.5 THE (Browse shelf(Opens below))	1	Available	BK002715
Books	Harare Institute of Technology Main Library	Harare Institute of Technology Main Library		Q325.5 THE (Browse shelf(Opens below))	2	Available	BK002535
Books	Harare Institute of Technology Main Library	Harare Institute of Technology Main Library		Q325.5 THE (Browse shelf(Opens below))	3	Available	BK002628
Books	Harare Institute of Technology Main Library	Harare Institute of Technology Main Library	General Collection	Q325.5 THE (Browse shelf(Opens below))	4	Available	BK003008

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.

"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.

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