Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, 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 learning is treated in detail with emphasis on the EM algorithm and its approximate variational versions with a focus on mixture modelling, regression and classification. Nonparametric Bayesian learning, including Gaussian, Chinese restaurant, and Indian buffet processes are also presented. Monte Carlo methods, particle filtering, probabilistic graphical models with emphasis on Bayesian networks and hidden Markov models are treated in detail. Dimensionality reduction and latent variables modelling are considered in depth. Neural networks and deep learning are thoroughly presented, starting from the perceptron rule and multilayer perceptrons and moving on to convolutional and recurrent neural networks, adversarial learning, capsule networks, deep belief networks, GANs, and VAEs. The book also covers the fundamentals on statistical parameter estimation and optimization algorithms.
Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all methods and techniques are explained in depth, supported by examples and problems, providing an invaluable resource to the student and researcher for understanding and applying machine learning concepts.
New to this edition
1. Chapter 1: Introduction - p. 1 2. Chapter 2: Probability and stochastic processes - p. 19 3. Chapter 3: Learning in parametric modeling: basic concepts and directions - p. 65 4. Chapter 4: Mean-square error linear estimation - p. 119 5. Chapter 5: Online learning: the stochastic gradient descent family of algorithms - p. 177 6. Chapter 6: The least-squares family - p. 249 7. Chapter 7: Classification: a tour of the classics - p. 293 8. Chapter 8: Parameter learning: a convex analytic path - p. 343 9. Chapter 9: Sparsity-aware learning: concepts and theoretical foundations - p. 417 10. Chapter 10: Sparsity-aware learning: algorithms and applications - p. 463 11. Chapter 11: Learning in reproducing kernel Hilbert spaces - p. 519 12. Chapter 12: Bayesian learning: inference and the EM algorithm - p. 583 13. Chapter 13: Bayesian learning: approximate inference and nonparametric models - p. 635 14. Chapter 14: Monte Carlo methods - p. 717 15. Chapter 15: Probabilistic graphical models: part I - p. 755 16. Chapter 16: Probabilistic graphical models: part II - p. 805 17. Chapter 17: Particle filtering - p. 855 18. Chapter 18: Neural networks and deep learning: part I - p. 885 19. Chapter 19: Neural networks and deep learning: part II - p. 997 20. Chapter 20: Dimensionality reduction and latent variable modeling - p. 1103 21. Index - p. 1181
Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, 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 learning is treated in detail with emphasis on the EM algorithm and its approximate variational versions with a focus on mixture modelling, regression and classification. Nonparametric Bayesian learning, including Gaussian, Chinese restaurant, and Indian buffet processes are also presented. Monte Carlo methods, particle filtering, probabilistic graphical models with emphasis on Bayesian networks and hidden Markov models are treated in detail. Dimensionality reduction and latent variables modelling are considered in depth. Neural networks and deep learning are thoroughly presented, starting from the perceptron rule and multilayer perceptrons and moving on to convolutional and recurrent neural networks, adversarial learning, capsule networks, deep belief networks, GANs, and VAEs. The book also covers the fundamentals on statistical parameter estimation and optimization algorithms.
Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all methods and techniques are explained in depth, supported by examples and problems, providing an invaluable resource to the student and researcher for understanding and applying machine learning concepts.
New to this edition
1. Chapter 1: Introduction - p. 1 2. Chapter 2: Probability and stochastic processes - p. 19 3. Chapter 3: Learning in parametric modeling: basic concepts and directions - p. 65 4. Chapter 4: Mean-square error linear estimation - p. 119 5. Chapter 5: Online learning: the stochastic gradient descent family of algorithms - p. 177 6. Chapter 6: The least-squares family - p. 249 7. Chapter 7: Classification: a tour of the classics - p. 293 8. Chapter 8: Parameter learning: a convex analytic path - p. 343 9. Chapter 9: Sparsity-aware learning: concepts and theoretical foundations - p. 417 10. Chapter 10: Sparsity-aware learning: algorithms and applications - p. 463 11. Chapter 11: Learning in reproducing kernel Hilbert spaces - p. 519 12. Chapter 12: Bayesian learning: inference and the EM algorithm - p. 583 13. Chapter 13: Bayesian learning: approximate inference and nonparametric models - p. 635 14. Chapter 14: Monte Carlo methods - p. 717 15. Chapter 15: Probabilistic graphical models: part I - p. 755 16. Chapter 16: Probabilistic graphical models: part II - p. 805 17. Chapter 17: Particle filtering - p. 855 18. Chapter 18: Neural networks and deep learning: part I - p. 885 19. Chapter 19: Neural networks and deep learning: part II - p. 997 20. Chapter 20: Dimensionality reduction and latent variable modeling - p. 1103 21. Index - p. 1181
