Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. ISBN 9781681733029, 207 pages.

This book provides a technical overview of adversarial machine learning. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. ISBN 9781681733951, 169 pages.

The first part of this book asks "are there voting rules that are truthful?" in that all voters have an incentive to report their true preferences. In the second, we ask "what would be the outcome when voters do vote strategically?" rather than preventing such behavior. We overview various game-theoretic models and equilibrium concepts, demonstrate how they apply to voting games, and discuss their implications on social welfare. ISBN 9781681733593, 167 pages.

In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying naturesfrom purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). ISBN 9781681732749, 150 pages.

Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, it becomes important to verify the correctness of data AND provide incentives so that agents providing high-quality data are rewarded while those that dont are discouraged by low rewards. ISBN 9781627057295, 151 pages.

Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize efficacy of the treatment while minimizing side effects. These objectives typically conflict, e.g., we can increase the efficacy of the treatment, but cause more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. ISBN 9781627059602, 129 pages.

This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. ISBN 9781627058414, 189 pages.

This book provides a tutorial introduction to modern techniques for representing and reasoning about qualitative preferences with respect to a set of alternatives. The syntax and semantics of several languages for representing preference languages, including CP-nets, TCP-nets, CI-nets, and CP-theories, are reviewed. Some key problems in reasoning about preferences are also introduced. ISBN 9781627058391, 154 pages.

This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. ISBN 9781627053655, 151 pages.

Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied. ISBN 9781627052016, 125 pages.