Zhang B Machine Learning and Visual Perception 2020 torrent

Textbook in PDF format

Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian learning, support vector machines, AdaBoost, compressive sensing and so on.
From the preface:
"With numerous books focusing on machine learning and visual perception, this book has been written with the aim of introducing the basic concepts discussed in other books, as well as expound on many primary algorithms and principles used in recent years, allowing readers to not only learn this basic knowledge but also identify the main direction it is moving in. Thus, the main works of the authors are presented as two primary aspects. On the one hand, the book presents the principles from the readers ’ perspective that might help them in learning, such as decision tree, Bayesian learning, SVM, compression perception, and deep learning. Unlike the tedious theories and complicated formulas in other books, this book emphasizes the practical aspects and explains a large number of implementation examples of algorithms in order to help readers understand them. On the other hand, each chapter of the book is relatively independent, which includes not only the traditional theories and methods but also novel algorithms published in recent conferences as well as popular deep learning theories, allowing students and readers alike to understand the fundamentals and new research directions in machine learning.
This book introduces the latest achievements in machine learning and reviews the research results of several authors. With the many years of studies on classifier design, face recognition, video understanding, palm pattern recognition, and railway image detection, the book would be helpful for researchers interested in the related fields."
Introduction.
Introduction of machine learning.
PAC Model.
Decision tree learning.
Bayesian learning.
Support vector machines.
AdaBoost.
Compressed sensing.
Subspace learning.
Deep learning and neural networks.
Reinforcement learning.
Bibliography.
Index