We know from experience and research that supervisory relationships can be immensely rewarding and developmental. Yet the same relationships can also be, and often are at the same time, highly anxiety-provoking and conflictual. Supervision as a developmental process is often mixed with quality assurance, performance reports, or marking and evaluation. Such processes only amplify the substantial power relationships that are part and parcel of supervision and they make engaging well with a supervisor really tough and challenging. This book helps supervisees to get the most out of supervision and reap the unique and substantial benefits that can indeed be found on this profound journey.

We live in a surveillance society. Anyone who uses a credit card, cell phone, or even search engines to navigate the Web is being monitored and assessed—and often in ways that are imperceptible to us. The first general introduction to the growing field of surveillance studies, SuperVision uses examples drawn from everyday technologies to show how surveillance is used, who is using it, and how it affects our world. Beginning with a look at the activities and technologies that connect most people to the surveillance matrix, from identification cards to GPS devices in our cars to Facebook, John Gilliom and Torin Monahan invite readers to critically explore surveillance as it relates to issues of law, power, freedom, and inequality. Even if you avoid using credit cards and stay off Facebook, they show, going to work or school inevitably embeds you in surveillance relationships. Finally, they discuss the more obvious forms of surveillance, including the security systems used at airports and on city streets, which both epitomize contemporary surveillance and make impossibly grand promises of safety and security. Gilliom and Monahan are among the foremost experts on surveillance and society, and, with SuperVision, they offer an immensely accessible and engaging guide, giving readers the tools to understand and to question how deeply surveillance has been woven into the fabric of our everyday lives.

A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.

David's visit is about a boy, his father, and their experience with supervised visitation.