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  3. Machine learning

Inference and Learning from Data: Volume 2

- Inference
Af: Ali H. Sayed Engelsk Hardback

Inference and Learning from Data: Volume 2

- Inference
Af: Ali H. Sayed Engelsk Hardback
Tjek vores konkurrenters priser
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 second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. 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), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, 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.
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Tjek vores konkurrenters priser
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 second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. 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), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, 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.
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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 1070
ISBN-13: 9781009218269
Indbinding: Hardback
Udgave:
ISBN-10: 1009218263
Kategori: Machine learning
Udg. Dato: 22 dec 2022
Længde: 46mm
Bredde: 252mm
Højde: 151mm
Forlag: Cambridge University Press
Oplagsdato: 22 dec 2022
Forfatter(e): Ali H. Sayed
Forfatter(e) Ali H. Sayed

Kategori Machine learning

ISBN-13 9781009218269

Sprog Engelsk

Indbinding Hardback

Sider 1070

Udgave

Længde 46mm

Bredde 252mm

Højde 151mm

Udg. Dato 22 dec 2022

Oplagsdato 22 dec 2022

Forlag Cambridge University Press

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