Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)
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Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)
An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality.
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)
Nº de artículo: 81427698

Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)

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Nº de artículo: 81427698

PYG 1493154

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An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality.
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What Stands Out

In-Depth Insights
Offers advanced analysis of probabilistic methods, enabling readers to grasp complex concepts efficiently and apply them in real-world scenarios.
Comprehensive Coverage
Covers a broad range of topics, from foundational principles to cutting-edge techniques, catering to both researchers and practitioners in machine learning.
Practical Applications
Integrates theoretical knowledge with practical applications, ensuring that readers can translate learning into actionable solutions in various fields.

Detalles de producto

Shop Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series) online at a best price in Paraguay. 0262048434
Publisher The MIT Press
Publication date August 15, 2023
Language English
Print length 1360 pages
ISBN-10 0262048434
ISBN-13 978-0262048439
Item Weight 4.98 pounds (2.26 kg)
Dimensions 8.39 x 2.17 x 9.29 inches (21.3 x 5.5 x 23.6 cm)

Who Should Buy?

Suitable For
  • Graduate Students

    Ideal for postgraduate students specializing in machine learning or statistics seeking advanced topics and theoretical depth.

  • Machine Learning Researchers

    Essential for researchers looking to deepen knowledge in probabilistic models and their applications in machine learning.

  • Data Scientists

    Beneficial for data scientists wanting to enhance their skills in probabilistic approaches for better decision-making and predictions.

Not Suitable For
  • Beginners

    Not suitable for those new to machine learning, as it assumes prior knowledge and expertise in advanced concepts.

DESCRIPCIÓN DEL PRODUCTO

Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)

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Preguntas y respuestas de los clientes

  • Pregunta: What is the focus of the book 'Probabilistic Machine Learning: Advanced Topics'?

    Respuesta: The book delves into advanced concepts of probabilistic machine learning, emphasizing models that handle uncertainty and make predictions. It covers probabilistic programming, Bayesian methods, and graphical models. This is crucial for fields like finance and healthcare, where decisions must be made under uncertainty.
  • Pregunta: Who is the target audience for this book?

    Respuesta: Primarily aimed at graduate students and professionals in machine learning or statistics, this book requires a foundational understanding of both subjects. It is particularly beneficial for those looking to deepen their expertise in probabilistic approaches, making it valuable for researchers and practitioners in AI.
  • Pregunta: What are some key concepts covered in the book?

    Respuesta: The book covers various key concepts including Bayesian inference, Markov chain Monte Carlo methods, and variational inference. These concepts provide tools for better model interpretability and robustness, which are vital in applications like predictive modeling and automated decision-making.
  • Pregunta: How does this book differ from other machine learning texts?

    Respuesta: Unlike many conventional machine learning texts that may focus on deterministic approaches, this book emphasizes the role of probability in model formation. By leveraging uncertainty, readers can develop more adaptable models, essential for real-world applications where data can be noisy or incomplete.
  • Pregunta: What skills can I expect to enhance by reading this book?

    Respuesta: Readers will enhance their capabilities in probabilistic reasoning and statistical modeling. Skills gained include building complex probabilistic models, utilizing Bayesian inference for predictions, and applying insights to navigate uncertainties in data-driven environments, making it an essential resource for data scientists.
  • Pregunta: Does the book include practical examples or case studies?

    Respuesta: Yes, it includes numerous practical examples and case studies illustrating the application of probabilistic models in various fields like computer vision and natural language processing. These examples aid in understanding complex theories while providing readers with real-world insights on model implementation.
  • Pregunta: Are there any prerequisites to reading 'Probabilistic Machine Learning: Advanced Topics'?

    Respuesta: A solid understanding of basic machine learning concepts and foundations in statistics is recommended. Familiarity with linear algebra and calculus will also be beneficial. This background will help readers fully appreciate the advanced topics discussed and their applications in professional scenarios.
  • Pregunta: Is this book suitable for self-study?

    Respuesta: Absolutely! The book is structured in a way that makes it ideal for self-study, with clear explanations and illustrative figures. Each chapter builds upon the last, enabling readers to progress through complex topics at their own pace, making it excellent for learners looking to expand their knowledge independently.
  • Pregunta: Can this book be helpful for industry applications?

    Respuesta: Yes, it is particularly useful for professionals working in AI, data analytics, and related fields. The probabilistic models and techniques discussed can be applied directly to enhance machine learning systems in industries such as finance, healthcare, and marketing, fostering innovation and more accurate predictions.
  • Pregunta: Where can I buy 'Probabilistic Machine Learning: Advanced Topics' in Paraguay?

    Respuesta: You can purchase 'Probabilistic Machine Learning: Advanced Topics' from Ubuy in Paraguay. Ubuy offers a seamless online shopping experience, making it easy to find and acquire this advanced text for your machine learning and academic needs.

Intelligence & Semantics Editorial Review

Probabilistic Machine Learning: Advanced Topics from the Adaptive Computation and Machine Learning series is a highly praised book among readers in the field of machine learning. The detailed and comprehensive content in this book provides an educational experience that readers find valuable. One reader even likened it to having a personal teacher available on their bookshelf, as the material encourages deep understanding by referring back to other sections and articles, ultimately allowing for continual learning with each read-through. The book is praised for its in-depth coverage of various topics in machine learning, including fundamentals, inference, prediction, generation, discovery, and action. The author's use of Bayesian perspectives and addressing a wide range of topics, from statistical machine learning to techniques for processing image, audio, and language data, has been appreciated. Additionally, the book is noted for its clear mathematical descriptions of key concepts, making it accessible for those seeking rigorous explanations. It also includes links to Git for accessing over 500 Python sample codes to delve deeper into the elements of the topics, thereby demonstrating a thoughtful and practical approach for further exploration. While there have been some comments regarding the quality of the physical product, particularly the image printing in the grayscale edition, the content and educational value of the book remain highly praised.

Customer Reviews & Ratings

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ventajas

  • Comprehensive coverage of advanced topics in machine learning
  • In-depth and rigorous mathematical explanations
  • Practical resources such as links to over 500 Python sample codes for further exploration

Contras

  • Some concerns about the quality of the physical product, particularly regarding the grayscale image printing

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características y beneficios

  • Researchers and graduate students in machine learning and statistics
  • Deep generative modeling
  • Graphical models
  • Bayesian inference
  • Reinforcement learning
  • Causality
  • Provides knowledge of crucial issues in machine learning from top scientists and domain experts
  • Puts deep learning in a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference

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