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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
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Item #:
128878998
LYD 1095
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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
| 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.
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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
Probabilistic Machine Learning: An Introduction (Adaptive Computation And Machine Learning Series) offers a comprehensive overview of the foundational principles and methods of probabilistic machine learning, making it an essential read for both students and professionals in the field. Published by The MIT Press, this extensive 944-page volume released on March 1, 2022, covers an expansive range of topics. Its in-depth content is suitable for those looking to enhance their knowledge of machine learning theories and techniques. The book has been praised for its clarity and thoroughness, providing readers with robust theoretical insights while maintaining accessibility.
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Pros
- Comprehensive overview of probabilistic methods
- Ideal for students and professionals
- Extensive 944 pages of content
- Published by a reputable press
- Clear and thorough explanations
Cons
- Some technical concepts may require prior knowledge
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