Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many tasks and application fields: planning, reasoning, robotics natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. This book presents coherently integrated chapters by leading authorities and assesses the state of the art in sequence learning by introducing essential models and algorithms and by examining a variety of applications. The book offers topical sections on sequence clustering and learning with Markov models, sequence prediction and recognition with neural networks, sequence discovery with symbolic methods, sequential decision making, biologically inspired sequence learning models.

Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.

"It is a pleasure to have a full length treatise on this most important topic, and may this focus on transfer become much more debated, taught, and valued in our schools." - John Hattie Teach students to use their learning to unlock new situations. How do you prepare your students for a future that you can’t see? And how do you do it without exhausting yourself? Teachers need a framework that allows them to keep pace with our rapidly changing world without having to overhaul everything they do. Learning That Transfers empowers teachers and curriculum designers alike to harness the critical concepts of traditional disciplines while building students’ capacity to navigate, interpret, and transfer their learning to solve novel and complex modern problems. Using a backwards design approach, this hands-on guide walks teachers step-by-step through the process of identifying curricular goals, establishing assessment targets, and planning curriculum and instruction that facilitates the transfer of learning to new and challenging situations. Key features include Thinking prompts to spur reflection and inform curricular planning and design. Next-day strategies that offer tips for practical, immediate action in the classroom. Design steps that outline critical moments in creating curriculum for learning that transfers. Links to case studies, discipline-specific examples, and podcast interviews with educators. A companion website that hosts templates, planning guides, and flexible options for adapting current curriculum documents. Using a framework that combines standards and the best available research on how we learn, design curriculum and instruction that prepares your students to meet the challenges of an uncertain future, while addressing the unique needs of your school community.

In Order to Learn shows how order effects are crucial in human learning, instructional design, machine learning, and both symbolic and connectionist cognitive models. Each chapter explains a different aspect of how the order in which material is presented can strongly influence what is learned by humans and theoretical models of learning in a variety of domains. In addition to data, models are provided that predict and describe order effects and analyze how and when they will occur.

Understanding Intuition: A Journey In and Out of Science explores the biological and cognitive mechanisms that account for intuition, and examines the first-person experience. The book integrates both scientific and personal perspectives on this important yet elusive mental capacity. It uses specific encounters to illustrate that intuition is enhanced when we can attend to the subtle aspects of our inner experiences, such as bodily sensations, images, and differing kinds of intuitive evaluative feelings, all of which may emerge no further than on the fringe of awareness. This awareness of subtle inner experiences helps forge a more fluid exchange between the unconscious and conscious minds, and allows readers to calibrate their own intuitions. Over the course of the book, readers will gain a deeper appreciation and respect for the unconscious mind and its potential sophistication, and even its potential wisdom. Understanding Intuition is a timely and critical resource for students and researchers in psychology, cognitive science, theology, women's studies, and neuroscience. - Stresses the powerful influence of the unconscious mind and its important adaptive role - Frames intuition as significant and novel unconscious insight - Presents a systematic framework for understanding different kinds of intuition - Examines the emotional underpinnings of intuition, giving special emphasis to the role of somatic feelings and their derivatives

Attention and Implicit Learning provides a comprehensive overview of the research conducted in this area. The book is conceived as a multidisciplinary forum of discussion on the question of whether implicit learning may be depicted as a process that runs independently of attention. The volume also deals with the complementary question of whether implicit learning affects the dynamics of attention, and it addresses these questions from perspectives that range from functional to neuroscientific and computational approaches. The view of implicit learning that arises from these pages is not that of a mysterious faculty, but rather that of an elementary ability of the cognitive systems to extract the structure of their environment as it appears directly through experience, and regardless of any intention to do so. Implicit learning, thus, is taken to be a process that may shape not only our behavior, but also our representations of the world, our attentional functions, and even our conscious experience. (Series B)