Theodoridis, Sergios,  1951- <br/><br/>
Machine learning :  a Bayesian and optimization perspective  
Sergios Theodoridis 
 - Second edition. 
 - United Kingdom :  Academic press ;  2020. 
 - 1 online resource :  illustrations (colour) 
<br/><br/> Previous edition: London: Academic Press, 2015. 
<br/><br/>1. Introduction 2. Probability and stochastic Processes 3. Learning in parametric Modeling: Basic Concepts and Directions 4. Mean-Square Error Linear Estimation 5. Stochastic Gradient Descent: the LMS Algorithm and it's Family 6. The Least-Squares Family 7. Classification: A Tour of the Classics 8. Parameter Learning: A Convex Analytic Path 9. Sparsity-Aware Learning: Concepts and Theoretical Foundations 10. Sparsity-Aware Learning: Algorithms and Applications 11. Learning in Reproducing Kernel Hilbert Spaces 12. Bayesian Learning: Inference and EM Algorithm 13. Bayesian Learning: Approximate Inference and nonparametric Models 14. Montel Carlo Methods 15. Probabilistic Graphical Models: Part 1, 16. Probabilistic Graphical Models: Part 2, 17. Particle Filtering 18. Neural Networks and Deep Learning 19. Dimensionality Reduction and Latent Variables Modeling 
<br/><br/><label>ISBN: </label>9780128188040 (ePub ebook) : 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Machine learning--Mathematical models
<br/><br/><label>Dewey Class. No.: </label>006.31 TH MA