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81427698
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)
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Item #:
81427698
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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.
Product Details
| 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?
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Graduate Students
Ideal for postgraduate students specializing in machine learning or statistics seeking advanced topics and theoretical depth.
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Machine Learning Researchers
Essential for researchers looking to deepen knowledge in probabilistic models and their applications in machine learning.
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Data Scientists
Beneficial for data scientists wanting to enhance their skills in probabilistic approaches for better decision-making and predictions.
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Beginners
Not suitable for those new to machine learning, as it assumes prior knowledge and expertise in advanced concepts.
Product Description
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)
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Customer Questions & Answers
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Question:
What is the focus of the book 'Probabilistic Machine Learning: Advanced Topics'?
Answer: 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. -
Question:
Who is the target audience for this book?
Answer: 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. -
Question:
What are some key concepts covered in the book?
Answer: 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. -
Question:
How does this book differ from other machine learning texts?
Answer: 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. -
Question:
What skills can I expect to enhance by reading this book?
Answer: 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. -
Question:
Does the book include practical examples or case studies?
Answer: 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. -
Question:
Are there any prerequisites to reading 'Probabilistic Machine Learning: Advanced Topics'?
Answer: 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. -
Question:
Is this book suitable for self-study?
Answer: 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. -
Question:
Can this book be helpful for industry applications?
Answer: 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. -
Question:
Where can I buy 'Probabilistic Machine Learning: Advanced Topics' in São Tomé and Príncipe?
Answer: You can purchase 'Probabilistic Machine Learning: Advanced Topics' from Ubuy in São Tomé and Príncipe. 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 (Adaptive Computation and Machine Learning series) is an extensive textbook published by The MIT Press on August 15, 2023, that spans 1360 pages and covers a breadth of advanced topics in machine learning. Readers have praised its depth and detail, especially regarding critical concepts like matrix calculus and gradient descent, making it suitable for those looking to deepen their understanding of ML. The engaging style of the author, Kevin Murphy, who is recognized for his ability to blend teaching with research, contributes to the book’s appeal. While the book is primarily aimed at graduate students, even those with foundational knowledge in regression can benefit, despite the learning curve involved. The accompanying online resources enhance the learning experience, although some readers mentioned minor issues like errors in hardcopy versions versus updates on the author's website.
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Pros
- Comprehensive coverage of advanced machine learning topics
- Engaging teaching style from a renowned author
- Valuable online resources complement the textbook
- Impressive graphics enhance understanding
- Suitable for both beginners and experienced learners
Cons
- Hardcopies may contain typos not present in online versions
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Features & Benefits
- 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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