BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Inference and Learning from Data: Foundations: Volume 1

Inference and Learning from Data: Foundations: Volume 1

Inference and Learning from Data: Foundations: Volume 1
Stock photo: cover may vary

Inference and Learning from Data: Foundations: Volume 1 Hardback - 2022

by Ali H. Sayed

Add to wish list
  • New
  • Hardback
New

Description

Cambridge University Press, 2022. Hardcover. New. 1010 pages. 9.50x7.00x1.75 inches.
Ask the seller a question Add to wish list
A$304.44
A$29.34 Delivery to USA
Standard delivery: 7 to 14 days
More delivery options
Ships from Revaluation Books (Devon, United Kingdom)

Details

About Revaluation Books Devon, United Kingdom

Biblio member since 2020

General bookseller of both fiction and non-fiction.

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from Revaluation Books

Reader reviews for Inference and Learning from Data: Foundations: Volume 1

From the publisher

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 is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference 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.
tracking-