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This volume takes a broad view of multimodality as it applies to a wide range of subject areas, curriculum design, and classroom processes to examine the ways in which multiple modes combine in contemporary classrooms and its subsequent impact on student learning. Grounded in a systemic functional linguistic framework and featuring contributions from scholars across educational and multimodal research, the book begins with a historical overview of multimodality’s place in Western education and then moves to a discussion of the challenges and rewards of integrating multimodal texts and ever-evolving technologies in a variety of settings, include primary, language, music, early childhood, Montessori, and online classrooms. As a state of the art of teaching and learning through different modalities in different educational contexts, this book is an indispensable resource for students and scholars in applied linguistics, multimodality, and language education.

Learning strategies --- Blended learning

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AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author-an expert in the field-presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection- essential elements of most applied projects. This important resource: -Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods -Presents R source code which shows how to apply and interpret many of the techniques covered -Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions -Contains useful information for effectively communicating with clients A volume in the popular Wiley Series in Probability and Statistics, Machine Learning: a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning. STEVEN W. KNOX holds a Ph. D. in Mathematics from the University of Illinois and an M.S. in Statistics from Carnegie Mellon University. He has over twenty years' experience in using Machine Learning, Statistics, and Mathematics to solve real-world problems. He currently serves as Technical Director of Mathematics Research and Senior Advocate for Data Science at the National Security Agency.

Machine learning --- Machine learning.

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Learning --- Adult learning --- Philosophy --- Learning - Philosophy

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Machine learning

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Machine Learning