What is Probabilistic Machine Learning?

Probabilistic Machine Learning is a subfield that applies probabilistic methods to build machine learning models, enabling them to incorporate uncertainty and make predictions based on incomplete data. This approach encompasses various techniques like Bayesian inference and probabilistic graphical models. It's particularly useful in scenarios where one needs to reason under uncertainty, such as in risk assessment, anomaly detection, and natural language processing.

Who is the target audience for this book?

The target audience for 'Probabilistic Machine Learning: An Introduction' includes students, researchers, and professionals in fields such as computer science, data science, and statistics. The book is designed for those with a foundational understanding of machine learning concepts and aims to provide deeper insights into probabilistic frameworks. It serves as an excellent resource for graduate-level courses or self-study, guiding readers through complex concepts with clarity.

What topics does this book cover?

This book covers a range of essential topics including Bayesian methods, Markov Chain Monte Carlo techniques, and variational inference. It delves into model evaluation, overfitting, and inference, along with practical applications. The structured approach allows readers to build a robust understanding, with case studies that illustrate real-world use cases. These aspects make it a valuable reference for anyone looking to enhance their expertise in machine learning.

Is prior knowledge in statistics necessary to understand this book?

While prior knowledge of statistics can be beneficial, 'Probabilistic Machine Learning: An Introduction' is structured to be accessible to readers with foundational knowledge in related fields. It introduces key concepts gradually and provides intuitive explanations. Those without a strong statistics background can still learn effectively, especially if they are willing to engage with supplementary resources. This makes it suitable for a broader audience eager to delve into probabilistic methods.

Can this book be useful for practical applications?

Absolutely! This book not only discusses theoretical foundations but also emphasizes practical applications through real-world examples. Readers can learn how to apply probabilistic models to various domains, such as finance, healthcare, and AI. For instance, probabilistic methods can be used to predict stock market trends or to assess patient risk in medical settings, making this knowledge highly applicable across multiple fields.

What makes this book different from other machine learning books?

The unique aspect of 'Probabilistic Machine Learning: An Introduction' is its focus on integrating probability theory with machine learning practices. Unlike other texts that may emphasize deterministic models, this book guides readers through the nuances of uncertainty and probabilistic reasoning. This critical perspective allows practitioners to better handle real-world challenges like missing data or noisy inputs, setting it apart from traditional resources.

Are there exercises and examples included in the book?

Yes, the book includes numerous exercises and examples that are designed to reinforce the concepts discussed in each chapter. These practical exercises provide readers with the opportunity to apply what they learn and get hands-on experience with probabilistic models. This interactive learning approach is especially beneficial for those who wish to deepen their understanding and gain confidence in applying these techniques.

What prerequisites should I have before reading this book?

Before diving into 'Probabilistic Machine Learning: An Introduction', it's suggested to have a basic understanding of linear algebra, calculus, and introductory machine learning concepts. Familiarity with programming can also enhance your experience, as many practical examples involve coding implementations. These prerequisites help readers navigate through the mathematical foundations and better grasp the application of probabilistic models.

How does this book approach complex topics?

The book approaches complex topics by breaking them into manageable sections and providing clear explanations accompanied by visual aids, such as diagrams and charts. This instructional design facilitates understanding by progressively building on earlier knowledge, allowing readers to grasp advanced concepts like Bayesian networks and graphical models in a structured manner. This method is particularly helpful for readers who may be intimidated by the complexity of the subject matter.

如何從 Ubuy 在線購物 Probabilistic Machine Learning: An Introduction？

從 Ubuy 在線購物 Probabilistic Machine Learning: An Introduction 非常簡單。. 您只需搜索產品，在結賬時選擇運輸方式，然後將其運送到您所在的位置。

Probabilistic Machine Learning: An Introduction 可以在 Hong Kong 在線購物嗎？

是的，您可以在 Ubuy Hong Kong 以合理的價格購買該產品。. Probabilistic Machine Learning: An Introduction 在本地不可用，但您可以信任我們的快遞服務。

下訂單後需要多長時間才能收到產品？

您訂購的產品的交貨時間根據您訂購的商品和您選擇的運輸方式而有所不同。. 結賬時會提到預計送貨時間，所以購物時請放心。

觀看原文

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

This book offers a detailed and up-to-date introduction to machine learning through the unifying lens of probabilistic modeling and Bayesian decision theory.

物品 #: 40164543

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

Q: Where can I buy Probabilistic Machine Learning: An Introduction in Hong Kong?

You can purchase 'Probabilistic Machine Learning: An Introduction' on Ubuy, a reliable online platform that offers a wide selection of books. Ubuy is known for providing a seamless shopping experience, making it convenient to find and buy educational materials. Simply visit their website and search for the book title to access the product and make your purchase with confidence.

88% of respondents would recommend this to a friend

物品 #: 40164543

HKD 916

Price Details

Excluding Shipping & Custom charges ( Shipping and custom charges will be calculated on checkout )

^{*All items will import from 美國}

供應情形 : 有存貨

從USA商店導入

QTY:

現在訂購，約可在下列時間收貨： Monday, 十月 19

Secured transaction

Our Top Logistics Partners

This book offers a detailed and up-to-date introduction to machine learning through the unifying lens of probabilistic modeling and Bayesian decision theory.

U-Care Warranty:

None

選擇一個計劃

Fast
Shipping

Free
Return^*

Secure Packaging

100% Original Products

PCI DSS Compliance

ISO 27001 Certified

Note: Step Down Voltage Transformer required for using electronics products of 美國 store (110-120). Recommended power converters 立即購買.

What Stands Out

Comprehensive Coverage

This book offers an extensive exploration of probabilistic methods in machine learning, catering to both beginners and advanced practitioners, making it a valuable resource in the field.

Practical Applications

The content integrates theory with practical examples, enabling readers to apply probabilistic concepts in real-world scenarios, enhancing their understanding and skill set in machine learning.

Adaptive Learning

Utilizing the Adaptive Computation approach, this book adapts to varying skill levels, ensuring that learners can progress at their own pace while gaining profound insights into machine learning techniques.

產品詳情

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	March 1, 2022
Language	English
Print length	864 pages
ISBN-10	0262046822
ISBN-13	978-0262046824
Item Weight	3.31 pounds (1.5 kg)
Dimensions	8.39 x 1.3 x 9.25 inches (21.3 x 3.3 x 23.5 cm)

Who Should Buy?

Students in AI
Ideal for undergraduate and graduate students seeking foundational knowledge in probabilistic methods for machine learning.
Data Scientists
Beneficial for data scientists who want to expand their expertise in probabilistic approaches to data modeling and analysis.
Research Professionals
Suitable for researchers looking for comprehensive insights and methodologies related to probabilistic machine learning techniques.

Beginners in Programming
Not ideal for those without programming experience as the content may require prior coding skills and mathematical understanding.

產品敘述

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

有疑問？與我們聊天

客戶問題與解答

題: 如何從 Ubuy 在線購物 Probabilistic Machine Learning: An Introduction？

回答: 從 Ubuy 在線購物 Probabilistic Machine Learning: An Introduction 非常簡單。. 您只需搜索產品，在結賬時選擇運輸方式，然後將其運送到您所在的位置。
題: Probabilistic Machine Learning: An Introduction 可以在 Hong Kong 在線購物嗎？

回答: 是的，您可以在 Ubuy Hong Kong 以合理的價格購買該產品。. Probabilistic Machine Learning: An Introduction 在本地不可用，但您可以信任我們的快遞服務。
題: 下訂單後需要多長時間才能收到產品？

回答: 您訂購的產品的交貨時間根據您訂購的商品和您選擇的運輸方式而有所不同。. 結賬時會提到預計送貨時間，所以購物時請放心。

Intelligence & Semantics Editorial Review

Probabilistic Machine Learning: An Introduction Adaptive Computation and Machine Learning series is an ideal book for graduate students and researchers interested in understanding the ML theory. The author uses clear, understandable language to explain complex probability concepts used in probabilistic ML. The book has a good mathematical foundation and explains the theories in an intuitive way with a perfect mix of visuals. The content is great and covers a range of recent topics, which has earned it high praise among reviewers. However, some customers received defective copies of the book, which were disappointing given the book's high price. The paper quality has also been criticised. The Pros and Cons of the book are:

Customer Reviews & Ratings

5.0

1 客戶評分

5 星

100%
4 星

0%
3 星

0%
2 星

0%
1 星

0%

評價此產品

與其他客戶分享您的想法

優點

Clear and understandable language.
Covers a range of recent topics.
Good mathematical foundation explained intuitively with visuals.
Ideal for graduate students and researchers.
Well-written and informative.

缺點

Some copies have been received defective.

Product Price History

重要資訊

限制：如跨國購買產品，請注意製造商的保固有可能無效；製造商服務選項可能無法使用；產品手冊、教學、以及安全警示可能不會是目的地國家的語言；產品（與附加材料）設計也許不會符合目的地國家的標準、規格、以及標示要求；且產品也許不會符合目的地國家的伏特數值與其他電力標準（需要使用合適的變壓器或轉接器）。收件者須負責確定該產品在目的地國家可合法進口。當在 Ubuy 或其聯盟夥伴網站上訂購時，收件者為記錄上的進口者，且必須遵從目的地國家的法律與規範。
由於 Ubuy 是一個全球搜尋引擎，並不是所有列於 Ubuy 的產品都有出售。產品須受出口／貿易法規規範。

HKD 916

現在訂購，約可在下列時間收貨： Monday, 十月 19

QTY:

Secured transaction

PCI DSS compliant and ISO 27001:2022 certified, with encrypted payments and full buyer protection on every order.

Our Top Logistics Partners

Fastest cross-border delivery

特色和優勢

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.

Ubuy Assurance

Experience worry-free shopping with 100% original products, PCI DSS-compliant payment security, ISO 27001-certified data protection, the fastest cross-border delivery, free returns ^*, and secure packaging on every order.

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

HKD 916

Fast Shipping

Free Return*

Secure Packaging

100% Original Products

PCI DSS Compliance

ISO 27001 Certified

What Stands Out

產品詳情

Who Should Buy?

產品敘述

客戶問題與解答

Intelligence & Semantics Editorial Review

Customer Reviews & Ratings

評價此產品

優點

缺點

Product Price History

重要資訊

HKD 916

特色和優勢

Ubuy Assurance

Fast
Shipping

Free
Return^*