Author(s) Name:  Sergios Theodoridis

About the Book:

   Machine Learning: A Bayesian and Optimization Perspective, 2nd 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. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization.
   Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python.

Table of 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

ISBN:  9780128188040

Publisher:  Academic Press Publisher

Year of Publication:  2020

Book Link:  Home Page Url