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128878998
Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
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Item #:
128878998
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This book offers a detailed and up-to-date introduction to machine learning through the unifying lens of probabilistic modeling and Bayesian decision theory.
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What Stands Out
Comprehensive Coverage
This book offers a thorough exploration of probabilistic machine learning, making complex concepts accessible for both newcomers and seasoned practitioners in the field.
Adaptive Computation Series
As part of the respected Adaptive Computation and Machine Learning series, this title ensures authoritative content that is up-to-date with the latest research and methodologies.
Practical Applications
With real-world examples and applications, this guide bridges theory and practice, enabling readers to apply probabilistic methods effectively in their own projects.
Product Details
- Comprehensive introduction to machine learning presented through the lens of probabilistic modeling and Bayesian decision theory
- Covers mathematical background, basic supervised learning, and advanced topics including transfer learning and unsupervised learning
- Includes end-of-chapter exercises for practical application
- Completely new book reflecting the latest developments in the field, particularly deep learning
- Accompanied by online Python code for reproducing figures and practical implementation
- Part of a series, with a sequel covering more advanced topics in the same probabilistic approach
| Publisher | The MIT Press |
| Publication date | 1 Mar. 2022 |
| Language | English |
| Print length | 944 pages |
| ISBN-10 | 0262046822 |
| ISBN-13 | 978-0262046824 |
| Dimensions | 21.3 x 3.3 x 23.5 cm |
Who Should Buy?
-
Aspiring Data Scientists
Perfect for beginners wanting foundational knowledge in probabilistic methods for machine learning applications.
-
Machine Learning Researchers
Provides advanced insights and techniques for researchers working on algorithm development and statistical modeling.
-
Graduate Students
Ideal for graduate-level courses focusing on machine learning and statistical inference in academic settings.
-
Casual Readers
Not suitable for casual readers seeking light or general information about machine learning without in-depth technicalities.
Product Description
Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
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English edition Kevin P. Murphy Format: Hardcover Editorial Review
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Pros
- Clear explanations of concepts
- Detailed examples and exercises
- Well-organized structure
- Engaging writing style
- Suitable for beginners and experts
Cons
- Some sections may feel dense.
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Features & Benefits
- Comprehensive introduction to machine learning and deep learning.
- Focus on probabilistic modeling and Bayesian decision theory.
- Covers essential mathematical backgrounds, including linear algebra and optimization.
- Includes practical exercises at the end of each chapter for real-world application.
- Accompanied by online Python code for nearly all figures, enhancing learning.
- Prepares students for advanced topics in machine learning with a sequel.
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