Inference and Learning from Data

This three-volume textbook is a comprehensive introduction to mathematical and statistical techniques for data-driven learning and inference,with a focus on practical applications. It is ideal for early-career researchers and graduate students in signal processing, machine learning, statistical analysis, data science, and inference.

Format: Undefined
Length: 3370 pages
Publication date: 22 December 2022
Publisher: Cambridge University Press

This remarkable three-volume masterpiece, penned by a foremost authority in the field, offers an accessible and comprehensive foray into the diverse array of mathematical and statistical techniques that form the foundation of contemporary methods in data-driven learning and inference. The first volume, aptly titled Foundations, establishes essential topics in inference and learning, laying the groundwork for readers to delve into their practical applications. The second volume, Inference, introduces readers to cutting-edge techniques for inferring unknown variables and quantities, pushing the boundaries of knowledge. The final volume, Learning, provides a rigorous introduction to state-of-the-art learning methods, empowering students with the tools to excel in this rapidly evolving field. A consistent structure and pedagogy are employed throughout all three volumes, ensuring a deep understanding of the subject matter. With over 1,280 end-of-chapter problems (including solutions for instructors), over 600 figures, over 470 solved examples, datasets, and downloadable Matlab code, this textbook sequence is a treasure trove of knowledge for early-career researchers and graduate students across a wide range of courses, including signal processing, machine learning, statistical analysis, data science, and inference. Its unparalleled scale and depth make it an indispensable resource for anyone seeking to excel in the world of data-driven intelligence.

Weight: 5424g
Dimension: 197 x 259 x 127 (mm)
ISBN-13: 9781009218108