Sergios Theodoridis (professor of machine learning and signal proce

Machine Learning

A Bayesian and Optimization Perspective

• Gebundenes Buch

Produktbeschreibung

Presents the physical reasoning, mathematical modeling and algorithmic implementation of each method Updates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling Provides case studies on a variety of topics, including protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, and more

Produktdetails

• Verlag: Elsevier Science Publishing Co Inc
• 2 ed
• Seitenzahl: 1160
• Erscheinungstermin: 6. März 2020
• Englisch
• Abmessung: 241mm x 195mm x 63mm
• Gewicht: 2528g
• ISBN-13: 9780128188033
• ISBN-10: 0128188030
• Artikelnr.: 57357196

Autorenporträt

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.

Inhaltsangabe

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