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Museums and Interactive Virtual Learning
Museums and Interactive Virtual Learning provides informal educators with practical resources that will help them to build dynamic digital engagement experiences within their own cultural organizations. Presenting vignettes from experienced museum educators and end users, as well as scientific data and practical resources, the book highlights the mutual benefits that Interactive Virtual Learning (IVL) programs offer to the museum and those visiting from a distance.Chapters mirror the step-by-step process of developing reputable IVL programs and emphasize how important it is for cultural organizations to encourage cross-departmental collaboration, if they wish to ensure that their programs align with the overall goals of the organization.Providing a thorough overview of the technologies, budget, marketing and staff requirements, the authors offer a realistic depiction of the work involved in building content for digital engagement.Emphasizing the importance of assessing existing programming, the book shows how institutions can adapt content to fit a virtual format and create inclusive digital engagement opportunities that reach local, national, and international audiences. Museums and Interactive Virtual Learning is an essential guide for professionals who are tasked with interpreting the content of a cultural organization and building lasting digital engagement opportunities.It will be particularly useful to those looking to reach diverse audiences.
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Linear Algebra and Learning from Data
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Linear Algebra With Machine Learning and Data
This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining.The book offers a case study approach where each case will be grounded in a real-world application.This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on Decision Trees and their applications in regression analysis.The text can be considered in two different but overlapping general data analytics categories: clustering and interpolation.Knowledge of mathematical techniques related to data analytics and exposure to interpretation of results within a data analytics context are particularly valuable for students studying undergraduate mathematics.Each chapter of this text takes the reader through several relevant case studies using real-world data.All data sets, as well as Python and R syntax, are provided to the reader through links to Github documentation.Following each chapter is a short exercise set in which students are encouraged to use technology to apply their expanding knowledge of linear algebra as it is applied to data analytics.A basic knowledge of the concepts in a first Linear Algebra course is assumed; however, an overview of key concepts is presented in the Introduction and as needed throughout the text.
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Technologies, Innovation, and Change in Personal and Virtual Learning Environments
Over the last decade there continues to be an increase in the technology and how it affects our lives.Since then the incorporation of electronic databases and other communication tools for students, faculty and staff, virtual learning environments have become an important innovation in the student learning experience. Technologies, Innovation, and Change in Personal and Virtual Learning Environments presents a widespread collection of research on the growth, innovation and implementation of learning technologies for educators, technologists and trainers.The book is a useful source for academics and professionals interested in information and communication technologies.
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Linear Algebra And Optimization With Applications To Machine Learning - Volume I: Linear Algebra For Computer Vision, Robotics, And Machine Learning
This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering.By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem.At all times, pertinent real-world applications are provided.This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package.Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization.Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems.Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors).It encourages active learning with engaging 'explore' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice.A suite of computational notebooks offers a hands-on learning experience for students.This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics.
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Foundations of Linear and Generalized Linear Models
A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models.The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables.Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data.Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methodsAn overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problemsNumerous examples that use R software for all text data analysesMore than 400 exercises for readers to practice and extend the theory, methods, and data analysisA supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.
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Technology for the Language Classroom : Creating a 21st Century Learning Experience
This volume in the ALLC series offers current and soon-to-be professionals in the ESL / EFL field a comprehensive guide to how to make the best use of technology to enhance the English language learning experience.The book has a predominant focus on practical insights that are based on successful real-life experiences at the classroom and study program level, including contributions from teachers in various countries.Nevertheless, there is also a strong foundation in existing research and literature as they relate to the needs of English language teachers.To promote reflective and exploratory practice, there is plenty of 'food-for-thought' for the reader. Although pre-service and in-service teachers represent the primary audience, the book is likely to be just as useful for language program administrators, researchers, curriculum and materials writers, and e-learning developers.
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