Machine learning: A Bayesian and optimization perspective 2nd Edition

Machine learning: A Bayesian and optimization perspective 2nd Edition- Ebook Instant Download/Delivery ISBN(s): 9780128188033,0128188030,9780128188040, 0128188049

Product details:

• ISBN 10: 0128188049
• ISBN 13:9780128188040
• Author:Sergios Theodoridis
  

Machine Learning: A Bayesian and Optimization Perspective, 2^nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth.

Table contents:

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 its 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 the 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

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