Inference and Learning from Data. Volume 3, Learning
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- Nombre de pages3192
- PrésentationRelié
- FormatGrand Format
- Poids1.744 kg
- Dimensions18,0 cm × 25,0 cm × 4,0 cm
- ISBN978-1-009-21828-3
- EAN9781009218283
- Date de parution22/12/2022
- ÉditeurCambridge University Press
Résumé
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in Volume Ito provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, generalization theory, feedforward, convolutional, recurrent, and generative neural networks, meta learning, explainable learning, and adversarial attacks.
A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 100 solved examples, 280 figures, datasets, and download-able Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science, and inference.
A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 100 solved examples, 280 figures, datasets, and download-able Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science, and inference.
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in Volume Ito provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, generalization theory, feedforward, convolutional, recurrent, and generative neural networks, meta learning, explainable learning, and adversarial attacks.
A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 100 solved examples, 280 figures, datasets, and download-able Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science, and inference.
A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 100 solved examples, 280 figures, datasets, and download-able Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science, and inference.
