This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it …
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective …
Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse ?elds as computer vision, natural language …
The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and …
This self-contained, systematic treatment of multivariate approximation begins with classical linear approximation, and moves on to contemporary nonlinear approximation. It covers …
A comprehensive guide to restoring images degraded by motion blur, bridging the traditional approaches and emerging computational photography-based techniques, and bringing …
A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is …
This 2004 book is an accessible and comprehensive introduction to machine vision. It provides all the necessary theoretical tools and shows how they are applied in actual image …
Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for …
This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book …