Item #:
142563238
Pattern Recognition and Machine Learning (Information Science and Statistics)
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Item #:
142563238
KYD 118
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This textbook on Pattern Recognition presents the Bayesian viewpoint, approximate inference algorithms and graphical models to describe probability distributions.
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What Stands Out
Comprehensive Coverage
This book offers an in-depth exploration of pattern recognition and machine learning, addressing a wide range of techniques and methodologies suitable for both beginners and advanced practitioners.
Illustrated Concepts
Packed with helpful illustrations, the book effectively simplifies complex concepts, making it easier for readers to understand and apply theoretical knowledge to real-world scenarios.
Critical Insights
Provides valuable insights into the statistical foundations of machine learning, bridging the gap between theory and practical application, thus empowering readers to make informed decisions in their projects.
Product Details
- Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reacts these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
| Publisher | Springer |
| Publication date | 1 Jan. 2007 |
| Edition | 2006th |
| Language | English |
| Print length | 798 pages |
| ISBN-10 | 0387310738 |
| ISBN-13 | 978-0387310732 |
| Dimensions | 19.56 x 3.3 x 25.91 cm |
Who Should Buy?
-
Graduate Students
Ideal for students specializing in artificial intelligence, providing a comprehensive understanding of theory and applications.
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Data Scientists
Useful for professionals seeking to deepen their knowledge of machine learning techniques and pattern recognition methodologies.
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Research Professionals
Beneficial for researchers looking to explore advanced concepts and algorithms in pattern recognition and machine learning.
-
Beginners
Not suitable for novices, as it requires a solid understanding of statistics and mathematics to grasp concepts.
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English edition Christopher M. Bishop Format: Hardcover Editorial Review
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Features & Benefits
- Bayesian viewpoint in Pattern Recognition
- Fast approximate inference algorithms
- Use of graphical models to describe probability distributions
- Suitable for beginners with no prior knowledge
- Requires familiarity with multivariate calculus and basic linear algebra
- Includes a self-contained introduction to basic probability theory
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