Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning)
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning)
This book offers a detailed and up-to-date introduction to machine learning through the unifying lens of probabilistic modeling and Bayesian decision theory.
Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning)
Nº de artículo: 90353559

Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning)

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Nº de artículo: 90353559

USD 160

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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
Offers an extensive overview of probabilistic machine learning, making complex concepts accessible to both beginners and advanced practitioners, thus facilitating better understanding and application in various machine learning scenarios.
Real-World Applications
Includes practical examples and case studies, bridging theory and practice, allowing readers to apply learned concepts directly to real-world problems across diverse fields, enhancing practical learning experience.
Updated Insights
Published in 2022, it brings the latest advancements in the field, ensuring readers are equipped with current knowledge and methodologies, positioning them at the forefront of emerging trends in machine learning.

Detalles de producto

Shop Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning) online at a best price in Puerto Rico. 0262046822
  • 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 MIT Press
Publication date 1 Feb. 2022
Language English
Print length 944 pages
ISBN-10 0262046822
ISBN-13 978-0262046824
Item weight 1.5 kg
Dimensions 21.3 x 3.3 x 23.5 cm

Who Should Buy?

Suitable For
  • Graduate Students

    Ideal for graduate students pursuing machine learning, providing foundational principles in probabilistic methods.

  • Data Scientists

    Data scientists seeking to deepen their understanding of probabilistic models for better data interpretation and predictions.

  • Researchers

    Researchers exploring advanced machine learning techniques will find valuable insights and methodologies applicable to their studies.

Not Suitable For
  • Beginners

    Complete beginners may struggle with complex concepts without prior knowledge in statistics or machine learning.

DESCRIPCIÓN DEL PRODUCTO

Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning)

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Preguntas y respuestas de los clientes

  • Pregunta: What specific topics does the book cover?

    Respuesta: The book covers supervised learning, linear and logistic regression, deep neural networks, transfer learning, and unsupervised learning.
  • Pregunta: Is prior knowledge of machine learning required?

    Respuesta: No prior knowledge is required; it's designed as an introductory text.
  • Pregunta: Can I run the provided code on my computer?

    Respuesta: Yes, the code can be run inside a web browser using cloud-based notebooks.

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USD 160

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características y beneficios

  • 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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