Chapter: Bayesian Learning: Approximate Inference and Nonparametric Models

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Home > Machine Learning (Second Edition) > Bayesian Learning: Approximate Inference and Nonparametric Models

Machine Learning (Second Edition) |

Sergios Theodoridis

Academic Press

Bayesian Learning: Approximate Inference and Nonparametric Models

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Authors

Sergios Theodoridis

ARI Id

1665426553248_16760547

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Chapters/Headings	Author(s)	Pages	Info
Introduction	Sergios Theodoridis
Probability and Stochastic Processes	Sergios Theodoridis
Learning in Parametric Modeling: Basic Concepts and Directions	Sergios Theodoridis
Mean-Square Error Linear Estimation	Sergios Theodoridis
Online Learning: the Stochastic Gradient Descent Family of Algorithms	Sergios Theodoridis
The Least-Squares Family	Sergios Theodoridis
Classification: a Tour of the Classics	Sergios Theodoridis
Parameter Learning: a Convex Analytic Path	Sergios Theodoridis
Sparsity-Aware Learning: Concepts and Theoretical Foundations	Sergios Theodoridis
Sparsity-Aware Learning: Algorithms and Applications	Sergios Theodoridis
Learning in Reproducing Kernel Hilbert Spaces	Sergios Theodoridis
Bayesian Learning: Inference and the EM Algorithm	Sergios Theodoridis
Bayesian Learning: Approximate Inference and Nonparametric Models	Sergios Theodoridis
Monte Carlo Methods	Sergios Theodoridis
Probabilistic Graphical Models: Part I	Sergios Theodoridis
Probabilistic Graphical Models: Part II	Sergios Theodoridis
Particle Filtering	Sergios Theodoridis
Neural Networks and Deep Learning	Sergios Theodoridis
Dimensionality Reduction and Latent Variable Modeling	Sergios Theodoridis
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