Machine Learning

From the Classics to Deep Networks, Transformers, and Diffusion Models

Sergios Theodoridis (Autor)

Buch | Softcover

1200 Seiten

2025 | 3rd edition
Academic Press Inc (Verlag)
978-0-443-29238-5 (ISBN)

Noch nicht erschienen (ca. Februar 2025)
Versandkostenfrei
innerhalb Deutschlands
Auch auf Rechnung
Verfügbarkeit in der
Filiale vor Ort prüfen

Artikel merken

Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models, Third Edition presents the most updated information on topics including mean square, least squares, maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modeling 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.

In addition, dimensionality reduction and latent variables modeling are also considered in-depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. Finally, the book 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.

Sergios Theodoridis is professor of machine learning and signal processing with the National and Kapodistrian University of Athens, Athens, Greece and with the Chinese University of Hong Kong, Shenzhen, China. He has received a number of prestigious awards, including the 2014 IEEE Signal Processing Magazine Best Paper Award, the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award, the 2017 European Association for Signal Processing (EURASIP) Athanasios Papoulis Award, the 2014 IEEE Signal Processing Society Education Award, and the 2014 EURASIP Meritorious Service Award. He has served as president of EURASIP and vice president for the IEEE Signal Processing Society and as Editor-in-Chief IEEE Transactions on Signal processing. 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.

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

Erscheint lt. Verlag	1.2.2025
Verlagsort	San Diego
Sprache	englisch
Maße	191 x 235 mm
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Naturwissenschaften ► Physik / Astronomie ► Elektrodynamik
ISBN-10	0-443-29238-8 / 0443292388
ISBN-13	978-0-443-29238-5 / 9780443292385
Zustand	Neuware