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This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.

100 Ideas for Primary Teachers: Outdoor Learning is packed with inspiring ideas to generate enthusiasm and engage pupils in learning through positive outdoor experiences. These can then be used as a s

Online learning is ubiquitous for millions of students worldwide, yet our understanding of student experiences in online learning settings is limited. The geographic distance that separates faculty fr

A simple user-friendly book which helps complete beginners of all ages get started using the web languages, HTML, CSS and JavaScript. After finishing this book, readers will have built their own

Implementing evidence-based and innovative teaching practices can feel like juggling: If you have standards-based learning in one hand and social-emotional learning in the other, what do you do with c

Around the world, children embark on learning to read in their home language or writing system. But does their specific language, and how it is written, make a difference to how they learn? How is learning to read English similar to or different from learning in other languages? Is reading alphabetic writing a different challenge from reading syllabic or logographic writing? Learning to Read across Languages and Writing Systems examines these questions across seventeen languages representing the world's different major writing systems. Each chapter highlights the key features of a specific language, exploring research on learning to read, spell, and comprehend it, and on implications for education. The editors' introduction describes the global spread of reading and provides a theoretical framework, including operating principles for learning to read. The editors' final chapter draws conclusions about cross-linguistic universal trends, and the challenges posed by specific languages and

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

Blended learning offers educators the opportunity to reimagine teaching and learning. It allows teachers to partner with their students to assess, track, and reflect on learning. This partnership give

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha

Much has been written about the escalating intolerance of worldviews other than one's own. Reasoned arguments based on facts and data seem to have little impact in our increasingly post-truth culture dominated by social media, fake news, tribalism, and identity politics. Recent advances in the study of human cognition, however, offer insights on how to counter these troubling social trends. In this book, psychologist Jon F. Wergin calls upon recent research in learning theory, social psychology, politics, and the arts to show how a deep learning mindset can be developed in both oneself and others. Deep learning is an acceptance that our understanding of the world around us is only temporary and is subject to constant scrutiny. Someone who is committed to learning deeply does not simply react to experiences, but engages fully with that experience, knowing that the inevitable disquietude is what leads to efficacy in the world.

Much has been written about the escalating intolerance of worldviews other than one's own. Reasoned arguments based on facts and data seem to have little impact in our increasingly post-truth culture dominated by social media, fake news, tribalism, and identity politics. Recent advances in the study of human cognition, however, offer insights on how to counter these troubling social trends. In this book, psychologist Jon F. Wergin calls upon recent research in learning theory, social psychology, politics, and the arts to show how a deep learning mindset can be developed in both oneself and others. Deep learning is an acceptance that our understanding of the world around us is only temporary and is subject to constant scrutiny. Someone who is committed to learning deeply does not simply react to experiences, but engages fully with that experience, knowing that the inevitable disquietude is what leads to efficacy in the world.

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha

According to Bayesian epistemology, rational learning from experience is consistent learning, that is learning should incorporate new information consistently into one's old system of beliefs. Simon M. Huttegger argues that this core idea can be transferred to situations where the learner's informational inputs are much more limited than Bayesianism assumes, thereby significantly expanding the reach of a Bayesian type of epistemology. What results from this is a unified account of probabilistic learning in the tradition of Richard Jeffrey's 'radical probabilism'. Along the way, Huttegger addresses a number of debates in epistemology and the philosophy of science, including the status of prior probabilities, whether Bayes' rule is the only legitimate form of learning from experience, and whether rational agents can have sustained disagreements. His book will be of interest to students and scholars of epistemology, of game and decision theory, and of cognitive, economic, and computer sci

This book presents the theory of output-driven maps and provides a fresh perspective on the extent to which phonologies can be characterized in terms of restrictions on outputs. Closely related to traditional conceptions of process opacity, but differing in notable ways, the theory of output-driven maps applies equally to SPE-style ordered rules, Optimality Theory, and other phonological theories. It permits a formally rigorous analysis of the issues in Optimality Theory that is not possible with traditional process opacity. Also presented is a theory of phonological learning. Building on prior work on learning in Optimality Theory, the learning theory exploits the formal structure of output-driven maps to achieve learning that is far more computationally efficient than comparable prior approaches. In this book Bruce Tesar, one of the founders of the study of learnability in Optimality Theory, presents fresh perspectives in an accessible way for graduate students and academic researche

Learning to See = Seeing to Learn: Vision, Learning & Behavior by Dr. Patrick Quaid et al, will unlock the connection between eyesight, education, and behaviors in children. How can you have 20/20

An essential tool for new teachers and trainees who want to use learning theories to develop their practice. The text explores key learning theories in a pragmatic way and encourages focused reflectio

Learning, innovation and adaptation are not concepts that we necessarily associate with the British army of the First World War. Yet the need to learn from mistakes, to exploit new opportunities and to adapt to complex situations are enduring and timeless. This revealing work is the first institutional examination of the army's process for learning during the First World War. Drawing on organisational learning and management theories, Aimée Fox critiques existing approaches to military learning in wartime. Focused around a series of case studies, the book ranges across multiple operational theatres and positions the army within a broader context in terms of its relationships with allies and civilians to reveal that learning was more complex and thoroughgoing than initially thought. It grapples with the army's failings and shortcomings, explores its successes and acknowledges the inherent difficulties of learning in a desperate and lethally competitive environment.

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.Deep learning is an artificial intellige

Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to

In order to design and deliver effective learning and development initiatives, it is essential to understand how our brains process and retain information. Neuroscience for Learning and Development in

Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabiliti

Assessing Deeper Learning explores how performance assessment advances deeper learning—the skills students need for 21st century success. The book examines different aspects of performance asses

Assessing Deeper Learning explores how performance assessment advances deeper learning—the skills students need for 21st century success. The book examines different aspects of performance asses

Professional Learning Communities charts a streamlined process for creating a PLC. The highest mission of a professional learning community is academic achievement. During Dr. Culp's forty-plus years

Written for teachers—and everyone interested in teaching and learning—Learning Deepens helps classrooms flourish by fostering students’ intrinsic motivation. By interleaving psycholo

Learning by Expanding challenges traditional theories that consider learning to be a process of acquisition and reorganization of cognitive structures within the closed boundaries of specific tasks or problems. Yrjö Engeström argues that this type of learning increasingly fails to meet the challenges of complex social change and fails to create novel artifacts and ways of life. In response, he presents an innovative theory of expansive learning activity, offering a foundation for understanding and designing learning as a transformation of human activities and organizations. The second edition of this seminal text features a substantive new introduction that illustrates the development and implementation of Engeström's theory since its inception.

"Why are you learning Zulu?" When Mark Sanders began studying the language, he was often asked this question. In Learning Zulu, Sanders places his own endeavors within a wider context to uncover how,

As the complexity of 21st century disciplines has increased it has become necessary to rethink the design and delivery of learning to more effectively serve the diverse needs and learning styles of st

Discover the fun learning habits of your favorite animals. Video game-playing pigs, copycat eagles, playful fox pups and many, many more fill these bright an whimsical pages! How do you like to learn?

Why have many developing countries that have succeeded in expanding access to education made such limited progress on improving learning outcomes? There is a growing recognition that the learning cris

Written by leading researchers in educational and social psychology, learning science, and neuroscience, this edited volume is suitable for a wide-academic readership. It gives definitions of key terms related to motivation and learning alongside developed explanations of significant findings in the field. It also presents cohesive descriptions concerning how motivation relates to learning, and produces a novel and insightful combination of issues and findings from studies of motivation and/or learning across the authors' collective range of scientific fields. The authors provide a variety of perspectives on motivational constructs and their measurement, which can be used by multiple and distinct scientific communities, both basic and applied.

Written by leading researchers in educational and social psychology, learning science, and neuroscience, this edited volume is suitable for a wide-academic readership. It gives definitions of key terms related to motivation and learning alongside developed explanations of significant findings in the field. It also presents cohesive descriptions concerning how motivation relates to learning, and produces a novel and insightful combination of issues and findings from studies of motivation and/or learning across the authors' collective range of scientific fields. The authors provide a variety of perspectives on motivational constructs and their measurement, which can be used by multiple and distinct scientific communities, both basic and applied.

Are policymakers capable of learning about the complex international environment they must deal with when formulating foreign policy? Interest in the phenomenon of "learning" has been growing, driven

A guide to both theory and practice of blended learning offering rigorous research, case studies, and methods for the assessment of educational effectiveness.Blended learning combines traditional in-p

This book provides a unique longitudinal account of content and language integrated learning (CLIL). Giving voice to both learners and teachers, it offers insights into language learning outcomes, lea

This book provides a unique longitudinal account of content and language integrated learning (CLIL). Giving voice to both learners and teachers, it offers insights into language learning outcomes, lea

The goal of Universal Design for Learning (UDL) is to use a variety of teaching methods to remove any barriers to learning and give all students equal opportunities to succeed. UDL doesn’t specificall

You’ve just found your new comprehensive guide to designing powerful professional learning!Full of protocols, vignettes, and case studies, this book dissects elements of professional learning, like co

Critiquing Social and Emotional Learning offers a systematic cultural analysis of social and emotional learning (SEL) as a codified phenomenon in education, critiquing the messages of SEL and the impa

Reflecting on Service-Learning in Higher Education examines forms of pedagogy such as service-learning, experiential learning, and problem-based learning in order to determine how students make connec