Self-contained chapters: Enables readers to learn without referring to external sources, making it ideal for self-study
Detailed exercises with online solutions: Reinforces understanding and provides support for problem-solving
Concrete examples throughout: Enhances intuitive grasp of concepts
Historical context on method development: Offers deeper insight into the evolution of supervised learning
Focus on Bayes decision rule: Foundations of the field are emphasized for a solid starting point
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Springer Asia Pacific Mathematics Series Pattern Recognition and Machine Learning for Self-Study I Supervised Learning Kenichiro Ishii | Naonori Ueda | Eisaku Maeda | Hiroshi Murase Mathematics / Applied
This book explains the basic principles of pattern recognition (PR) and machine learning (ML) in an easy-to-understand manner for beginners