Inference and Learning from Data Foundations, Volume 1
- Textbook
Description
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 first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy…
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Additional help with this eTextbook
This is the first volume of a three volume textbook. We encourage instructors to refer to the Course Guide in the Resources area for more information on how the volumes can be combined or used individually to support different courses.
Key features
- Unique in its scale and depth, this is a comprehensive introduction to methods in data-driven learning and inference
- Over 1300 end-of-chapter problems (with complete solutions for instructors), 600 figures and 470 in-text solved examples
- A phenomenal contribution by a world authority in the field
- Covers sufficient topics across the volumes for the construction of a variety of courses covering a wide range of themes
About the book
- DOI https://doi.org/10.1017/9781009218146
- Subjects Communications and Signal Processing,Computer Science,Engineering,Machine Learning and Pattern Recognition
- Format: Hardback
- Publication date: 02 March 2023
- ISBN: 9781009218122
- Dimensions (mm): 244 x 170 mm
- Weight: 1.79kg
- Page extent: 1010 pages
- Availability: In stock
- Format: Digital
- Publication date: 17 February 2023
- ISBN: 9781009218146
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Online publication date: 17 March 2023
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Online publication date: 24 February 2023
Hardback publication date: 22 December 2022
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