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Machine Learning
From the Classics to Deep Networks, Transformers, and Diffusion Models
- 3rd Edition - February 1, 2025
- Author: Sergios Theodoridis
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 9 2 3 8 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 9 2 3 9 - 2
Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum li… Read more
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Request a sales quoteMachine 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
The new material includes an extended coverage of attention transformers, large language models, self-supervised learning and diffusion models.
Provides a number of case studies and applications on a variety of topics, such as target localization, channel equalization, image denoising, audio characterization, text authorship identification, visual tracking, change point detection, hyperspectral image unmixing, fMRI data analysis, machine translation, and text-to-image generation.
• Most chapters include a number of computer exercises in both MatLab and Python, and the chapters dedicated to deep learning include exercises in PyTorch.
New to this edition
The new material includes an extended coverage of attention transformers, large language models, self-supervised learning and diffusion models.
Graduate students taking a machine learning course out of electrical and computer engineering, mechanical engineering, computer science, and other engineering departments
1. Introduction
2. Probability and stochastic Processes
3. Learning in parametric Modelling: 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. Bayesan Learning: Inference and the EM Algorithm
13. Bayesan 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 Part I
19. Neural Networks and Deep Learning:Part II
20. Dimensionality Reduction and Latent Variables Modeling
2. Probability and stochastic Processes
3. Learning in parametric Modelling: 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. Bayesan Learning: Inference and the EM Algorithm
13. Bayesan 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 Part I
19. Neural Networks and Deep Learning:Part II
20. Dimensionality Reduction and Latent Variables Modeling
- No. of pages: 1200
- Language: English
- Edition: 3
- Published: February 1, 2025
- Imprint: Academic Press
- Paperback ISBN: 9780443292385
- eBook ISBN: 9780443292392
ST
Sergios Theodoridis
Sergios Theodoridis is professor emeritus of machine learning and data processing with the National and Kapodistrian University of Athens, Greece. He is a Fellow of EURASIP and a Life Fellow of IEEE. He is the coauthor of the best-selling book Pattern Recognition, 4th edition, Academic Press, 2009, and of the book Introduction to Pattern Recognition: A MATLAB Approach, Academic Press, 2010.
Affiliations and expertise
Professor of Machine Learning and Signal Processing, National and Kapodistrian University of Athens, Athens, Greece and Chinese University of Hong Kong, Shenzhen, China.