Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to dreate training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.

"The market-leading education textbook on learning theories, Human Learning, Sixth Edition, covers a broad range of concepts and is supported by the author's lucid and engaging writing style, which helps readers learn the book's content meaningfully. In this new sixth edition, readers will find significant updates to reflect the most current research in the field, including: expansion of the chapter on cognition and memory; re-organization of content on Piaget and Vygotsky into two separate chapters; a core section on teaching critical-thinking skills; and the significantly revised discussion of technology-based instructed. Instructors and students alike can feel confident in learning about learning with this influential and best-selling author"--Publisher's website.

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.

Orchestrating learning that is bodybrain-compatible must be the foundation for what goes on in the classroom. Hart brilliantly explains the biology of learning related to classroom practice and allows the reader to "see" what is necessary for real reform efforts to succeed. The reader comes to appreciate how the brain makes meaning through pattern recognition, prepares to act through mental programs, and responds to emotion.

Categories of Human Learning covers the papers presented at the Symposium on the Psychology of Human Learning, held at the University of Michigan, Ann Arbor on January 31 and February 1, 1962. The book focuses on the different classifications of human learning. The selection first offers information on classical and operant conditioning and the categories of learning and the problem of definition. Discussions focus on classical and instrumental conditioning and the nature of reinforcement; comparability of the forms of human learning; conditioning experiments with human subjects; and subclasses of classical and instrumental conditioning. The text then takes a look at the representativeness of rote verbal learning and centrality of verbal learning. The publication ponders on probability learning, evaluation of stimulus sampling theory, and short-term memory and incidental learning. Topics include short-term retention, stimulus variation experiments, reinforcement schedules and mean response, systematic interpretations, and methodological approaches. The book then examines the behavioral effects of instruction to learning, verbalizations and concepts, and the generality of research on transfer functions. The selection is highly recommended for psychologists and educators wanting to conduct studies on the categories of human learning.

Learning is among the most basic of human activities. The study of, and research into, learning forms a central part of educational studies. The well-respected and established authors, Jarvis and Parker, not only focus on the psychological processes of human learning, but they also examine the importance of the relationship between the body and the mind. For the first time, this book considers how our neurological, biological, emotional and spiritual faculties all impact on human learning. Topics covered include: the biology of learning personality and human learning thinking and learning styles gender and human learning life cycle development and human learning emotional intelligence and learning morality and human learning learning in the social context. Drawing on material from the worlds of science and social science, and with contributions from international authors, this book will be of interest to academics in a wide range of disciplines.