Description
Pattern Recognition and Machine Learning is a textbook that offers a comprehensive introduction to the fields of pattern recognition and machine learning. These fields have seen substantial development over the past decade, with Bayesian methods transitioning from a niche area to mainstream application. The book covers the emergence of graphical models as a framework for probabilistic models and discusses the practical applicability of Bayesian methods enhanced by approximate inference algorithms like variational Bayes and expectation propagation.
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The textbook also explores new models based on kernels, which have significantly impacted algorithms and applications. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. The book assumes no prior knowledge of pattern recognition or machine learning concepts but requires familiarity with multivariate calculus and basic linear algebra. A self-contained introduction to basic probability theory is included, making it accessible to those with limited probability knowledge.
Pattern Recognition and Machine Learning serves as a valuable resource for understanding the recent developments in these fields. It provides insights into the integration of engineering and computer science perspectives, highlighting the dual facets of pattern recognition and machine learning. The textbook is published by Springer Nature and is available for purchase or borrowing through their platform.
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Book Details
Comprehensive introduction to pattern recognition
Covers machine learning developments
Focus on Bayesian methods
Includes graphical models framework
Discusses kernel-based models
Designed for advanced undergraduates and PhD students
Requires knowledge of calculus and linear algebra
Self-contained introduction to probability theory
Who This Book Is For
- Academic study
- Research reference
- Professional development
- Algorithm development
- Bayesian methods application
