Peng Liu

This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization. The book begins by introducing different Bayesian Optimization (BO) techniques, covering both commonly used tools and advanced topics.

It follows a “develop from scratch” method using Python, and gradually builds up to more advanced libraries such as BoTorch, an open-source project introduced by Facebook recently. Along the way, you’ll see practical implementations of this important discipline along with thorough coverage and straightforward explanations of essential theories. This book intends to bridge the gap between researchers and practitioners, providing both with a comprehensive, easy-to-digest, and useful reference guide. After completingthis book, you will have a firm grasp of Bayesian optimization techniques, which you’ll be able to put into practice in your own machine learning models. What You Will LearnApply Bayesian Optimization to build better machine learning modelsUnderstand and research existing and new Bayesian Optimization techniquesLeverage high-performance libraries such as BoTorch, which offer you the ability to dig into and edit the inner workingDig into the inner workings of common optimization algorithms used to guide the search process in Bayesian optimization. Who This Book Is ForBeginner to intermediate level professionals in machine learning, analytics or other roles relevant in data science.

Today’s cyber defenses are largely static allowing adversaries to pre-plan their attacks. In response to this situation, researchers have started to investigate various methods that make networked information systems less homogeneous and less predictable by engineering systems that have homogeneous functionalities but randomized manifestations. The 10 papers included in this State-of-the Art Survey present recent advances made by a large team of researchers working on the same US Department of Defense Multidisciplinary University Research Initiative (MURI) project during 2013-2019.

This project has developed a new class of technologies called Adaptive Cyber Defense (ACD) by building on two active but heretofore separate research areas: Adaptation Techniques (AT) and Adversarial Reasoning (AR). AT methods introduce diversity and uncertainty into networks, applications, and hosts. AR combines machine learning, behavioral science, operations research, control theory, and gametheory to address the goal of computing effective strategies in dynamic, adversarial environments.