Machine Learning: A Bayesian and Optimization Perspective 2nd Edition Sergios Theodoridis 0128188049 9780128188040

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Product details:

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

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 of contents:

Chapter 1: Introduction

Chapter 2: Probability and Stochastic Processes

Chapter 3: Learning in Parametric Modeling: Basic Concepts and Directions

Chapter 4: Mean-Square Error Linear Estimation

Chapter 5: Online Learning: the Stochastic Gradient Descent Family of Algorithms

Chapter 6: The Least-Squares Family

Chapter 7: Classification: a Tour of the Classics

Chapter 8: Parameter Learning: a Convex Analytic Path

Chapter 9: Sparsity-Aware Learning: Concepts and Theoretical Foundations

Chapter 10: Sparsity-Aware Learning: Algorithms and Applications

Chapter 11: Learning in Reproducing Kernel Hilbert Spaces

Chapter 12: Bayesian Learning: Inference and the EM Algorithm

Chapter 13: Bayesian Learning: Approximate Inference and Nonparametric Models

Chapter 14: Monte Carlo Methods

Chapter 15: Probabilistic Graphical Models: Part I

Chapter 16: Probabilistic Graphical Models: Part II

Chapter 17: Particle Filtering

Chapter 18: Neural Networks and Deep Learning

Chapter 19: Dimensionality Reduction and Latent Variable Modeling

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