Inference and Learning from Data

Ali H. Sayed

ISBN 10: 1009218107 / ISBN 13: 9781009218108

Published by Cambridge University Press Dez 2022, 2022

About this Item

Neuware - This extraordinary three-volume work provides an accessible, comprehensive introduction to mathematical and statistical techniques for data-driven learning and inference. Ideal for early-career researchers and graduate students across signal processing, machine learning, statistics and data science.

Seller Inventory # 9781009218108

Bibliographic Details

• Title: Inference and Learning from Data
• Publisher: Cambridge University Press Dez 2022
• Publication Date: 2022
• Language: English
• ISBN 10: 1009218107
• ISBN 13: 9781009218108
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 290W x 216H x 162D millimeters 290 millimeters width by 216 millimeters height by 162 millimeters depth
• Weight: 5408 grams

About this title

Synopsis

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. The first volume, Foundations, establishes core topics in inference and learning, and prepares readers for studying their practical application. The second volume, Inference, introduces readers to cutting-edge techniques for inferring unknown variables and quantities. The final volume, Learning, provides a rigorous introduction to state-of-the-art learning methods. A consistent structure and pedagogy is employed throughout all three volumes to reinforce student understanding, with over 1280 end-of-chapter problems (including solutions for instructors), over 600 figures, over 470 solved examples, datasets and downloadable Matlab code. 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.

About the Author

Ali H. Sayed is Professor and Dean of Engineering at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He has also served as Distinguished Professor and Chairman of Electrical Engineering at the University of California, Los Angeles, USA, and as President of the IEEE Signal Processing Society. He is a member of the US National Academy of Engineering (NAE) and The World Academy of Sciences (TWAS), and a recipient of the 2022 IEEE Fourier Award and the 2020 IEEE Norbert Wiener Society Award. He is a Fellow of the IEEE.