bayesoptbook.com

What It Is

Bayesian Optimization Book is a monograph on Bayesian optimization written by Roman Garnett and published by Cambridge University Press in 2023. The website offers multiple PDF versions of the book for download and states that it is available free of charge for personal use; the print edition can be ordered through Cambridge University Press or Amazon. It is not a traditional video course or bootcamp, but rather a systematic learning resource in the form of an English-language textbook/academic monograph.

Core Content

Judging from the main text, the book is positioned as a self-contained, comprehensive introduction “from scratch,” aiming to gradually develop the key ideas behind Bayesian optimization. The content is divided into three major parts: the theory and practice of Gaussian process modeling, Bayesian sequential decision-making methods, and the implementation of practical and effective optimization strategies. The table of contents also covers model assessment and selection, utility functions in optimization, common Bayesian optimization strategies, implementation, theoretical analysis, extended settings, a brief history of Bayesian optimization, and an annotated bibliography of applications.

Pricing and Access

The electronic version is freely available on the website for personal use, with three formats provided: 8×10, US Letter, and A4, suitable for online reading and printing respectively. The print book is available for purchase, but the extracted text does not include pricing or specific payment methods.

Pros and Cons

Its strengths include the authority of the publisher, a complete structure, coverage from theory to implementation, and a free electronic version that significantly lowers the barrier to learning. GitHub feedback and the errata list also help with ongoing corrections. Its limitations are that it is primarily an English-language monograph and does not include video lectures, assignment grading, quizzes, a community, or certificates. For learners with insufficient background in mathematics, statistics, or machine learning, the entry barrier may be relatively high.

Who It’s For

It is best suited for graduate students and researchers in machine learning, statistics, and related fields. It is also suitable for practitioners who need to understand the principles of Bayesian optimization in scenarios such as experimental design, hyperparameter tuning, and black-box optimization.

Access in China

The main text does not provide information on regional access or mirrors, so the stability of access from mainland China cannot be determined and is marked as unknown.