Pattern Recognition and Machine Learning for Self-Study I Supervised Learning

This book explains the basic principles of pattern recognition (PR) and machine learning (ML) in an easy-to-understand manner for beginners who are trying to learn these principles on their own. Readers with a basic knowledge of linear algebra and probability theory will find it easy to follow.

Many excellent books in this field have been published in the past.  However, these books are not necessarily intended for self-study by beginners.

This book limits the topics to the minimum essential themes that beginners should learn, and explains them in detail. This book focuses on supervised learning, first introducing classical but important methods that have contributed to the development of the field. It then explains various methods that have since attracted attention. In explaining these methods, the book also provides a historical account of how new technologies were created as a result of combining classical ideas. The book emphasizes that Bayes decision rule is a fundamental concept in PR and ML.

The following points make this book suitable for self-study by beginners.
(1) The book is self-contained, so that the reader does not need to refer to other books or literature.  
(2) To deepen the reader's understanding, exercises are provided at the end of each chapter with detailed solutions available online.
(3) To promote the reader's intuitive understanding, the book presents as many concrete examples as possible.
(4) ‘Coffee Break’ columns introduce knowledge and know-how from the author's experience.

Unsupervised learning will be discussed in a sequel.

Part I Linear Classification.- Chapter 1 Basic Concepts of Pattern Recognition.- Chapter 2 Linear Discriminant Functions and their Learning.- Chapter 3 Learning based on Minimum Squared Error Criterion.- Chapter 4 Classifier Design.-Chapter 5 Feature Evaluation and Bayes Error.- Chapter 6 Transformation of Feature Space.- Part II Nonlinear Classification.- Chapter 7 Subspace Method.- Chapter 8 Generalized Linear Discriminant Functions.- Chapter 9 Potential Function Method.- Chapter 10 Support Vector Machines. Chapter 11 Kernel Methods.- Chapter 12 Neural Networks.- Part III Bayesian Unified Framework.- Chapter 13 Convolutional Neural Networks.- Chapter 14 Generalization of Learning Algorithms.- Chapter 15 Learning Algorithms and Bayes Decision Rule.

Kenichiro Ishii worked at NTT until 2003, after which he served as a professor at Nagoya University until 2012. He is currently a Professor Emeritus at Nagoya University.

Naonori Ueda worked at NTT until 2023. Since then, he has been serving as an NTT Research Professor.  He has also been concurrently working at RIKEN Center for Advanced Intelligence Project (AIP) as Deputy Director since 2016, and in 2023, he transitioned to a full-time position at RIKEN AIP.

Eisaku Maeda was with NTT until 2017 and has since been serving as a professor at Tokyo Denki University.

Hiroshi Murase worked at NTT until 2003, after which he served as a professor at Nagoya University until 2021. He is currently a Professor Emeritus at the same university.