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While an incredibly interesting topic (albeit from my biased perspective), Bayesian optimization may appear elusive to many machine learning practitioners. The main culprit is the accessibility and quality of available resources: a full, in-depth treatment of the topic can only be found in textbooks and research papers, while online tutorials and blog posts lack the depth, structure, and consistency necessary for a solid understanding. I remember when I first learned about Bayesian optimization, it took me a long time to synthesize what I had read from these various sources and understand what they meant as a whole.

This book sets out to address this problem for newcomers who’d like to learn about Bayesian optimization—after all, every book is what the author wishes had existed when they first learned a topic. In the text, you will find a practical guide to what Bayesian optimization is, how it facilitates decision-making under uncertainty to optimize expensive processes, what different variations of Bayesian optimization there are, and last but not least, how to implement them with code. To be able to hit the ground running immediately, you will need a firm grasp on core concepts in machine learning, statistics, and Bayesian probability, as well as experience in Python programming.